kittycad.models.ok_modeling_cmd_response

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>, 'config': {'title': 'OptionCameraDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragEnd:94483162010016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_drag_end.CameraDragEnd'>, 'config': {'title': 'CameraDragEnd'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_drag_end.CameraDragEnd'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_drag_end.CameraDragEnd'>>]}, 'ref': 'kittycad.models.camera_drag_end.CameraDragEnd:94483154336336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'CameraDragEnd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_end', 'schema': {'expected': ['camera_drag_end'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragEnd', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934f11a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74c12f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_end", }, expected_py: None, name: "literal['camera_drag_end']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d9fa50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CameraDragEnd", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCameraDragEnd", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragEnd", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6325050, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6325470, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c63c6170, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c63c6270, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c6326e20, ), path: LookupPath( [ S( "center", Py( 0x00007f42c6324ed0, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c63244b0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c6324f30, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c63c56f0, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c63c4bb0, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c63249f0, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c6324c90, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c63c5e70, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c63c52f0, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c6324570, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c6324450, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c6324480, ), path: LookupPath( [ S( "up", Py( 0x00007f42c63250e0, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "CameraDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d9fa50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraDragEnd", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6325410, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6325350, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74c12f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_end": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74c12f0, ), ], }, expected_repr: "'camera_drag_end'", name: "literal['camera_drag_end']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_end']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCameraDragEnd", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934f11a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCameraDragEnd", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.camera_drag_end.CameraDragEnd, type: Literal['camera_drag_end'] = 'camera_drag_end') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CameraDragEnd[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CameraDragEnd, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_end'], required=False, default='camera_drag_end')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['camera_drag_end'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove(**data)[source][source]

The response to the ‘CameraDragMove’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.camera_drag_move.CameraDragMove'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['camera_drag_move']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>, 'config': {'title': 'OptionCameraDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCameraDragMove:94483161998640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_drag_move.CameraDragMove'>, 'config': {'title': 'CameraDragMove'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_drag_move.CameraDragMove'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_drag_move.CameraDragMove'>>]}, 'ref': 'kittycad.models.camera_drag_move.CameraDragMove:94483154369440', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'CameraDragMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'camera_drag_move', 'schema': {'expected': ['camera_drag_move'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCameraDragMove', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934ee530, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92da7ba0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CameraDragMove", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74c11b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "camera_drag_move", }, expected_py: None, name: "literal['camera_drag_move']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCameraDragMove", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCameraDragMove", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63274e0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6327540, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c6412db0, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c64108b0, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c63a7300, ), path: LookupPath( [ S( "center", Py( 0x00007f42c63a7030, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c63a73f0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c63a6eb0, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c64120f0, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c6412630, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c63277b0, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c6327840, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c64130b0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c6410ab0, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c63276f0, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c6327720, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c6327690, ), path: LookupPath( [ S( "up", Py( 0x00007f42c6327870, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "CameraDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92da7ba0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraDragMove", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6327510, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6327660, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74c11b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "camera_drag_move": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74c11b0, ), ], }, expected_repr: "'camera_drag_move'", name: "literal['camera_drag_move']", }, ), validate_default: false, copy_default: false, name: "default[literal['camera_drag_move']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCameraDragMove", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934ee530, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCameraDragMove", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.camera_drag_move.CameraDragMove, type: Literal['camera_drag_move'] = 'camera_drag_move') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CameraDragMove[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CameraDragMove, required=True), 'type': FieldInfo(annotation=Literal['camera_drag_move'], required=False, default='camera_drag_move')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['camera_drag_move'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass(**data)[source][source]

The response to the ‘CenterOfMass’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.center_of_mass.CenterOfMass'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['center_of_mass']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>, 'config': {'title': 'OptionCenterOfMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCenterOfMass:94483162674736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.center_of_mass.CenterOfMass'>, 'config': {'title': 'CenterOfMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.center_of_mass.CenterOfMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.center_of_mass.CenterOfMass'>>]}, 'ref': 'kittycad.models.center_of_mass.CenterOfMass:94483141233968', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center_of_mass': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitLength'>, 'members': [UnitLength.CM, UnitLength.FT, UnitLength.IN, UnitLength.M, UnitLength.MM, UnitLength.YD], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_length.UnitLength:94483152519200', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'CenterOfMass', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'center_of_mass', 'schema': {'expected': ['center_of_mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCenterOfMass', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93593630, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92120d30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center_of_mass": SerField { key_py: Py( 0x00007f42c74c0eb0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92bc80a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007f42c71c2f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee92be4020, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "CenterOfMass", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74c0eb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "center_of_mass", }, expected_py: None, name: "literal['center_of_mass']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCenterOfMass", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCenterOfMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61641b0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6164540, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center_of_mass", lookup_key: Simple { key: "center_of_mass", py_key: Py( 0x00007f42c64e8df0, ), path: LookupPath( [ S( "center_of_mass", Py( 0x00007f42c61878f0, ), ), ], ), }, name_py: Py( 0x00007f42c74c0eb0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92bc80a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point3d", }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f42c6187970, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f42c6187930, ), ), ], ), }, name_py: Py( 0x00007f42c71c2f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee92be4020, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "in": 2, "cm": 0, "m": 3, "yd": 5, "mm": 4, "ft": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c6a0e6f0, ), Py( 0x00007f42c6a0ec90, ), Py( 0x00007f42c6a0ed50, ), Py( 0x00007f42c6a0edb0, ), Py( 0x00007f42c6a0ee10, ), Py( 0x00007f42c6a0ee70, ), ], }, missing: None, expected_repr: "'cm', 'ft', 'in', 'm', 'mm' or 'yd'", strict: false, class_repr: "UnitLength", name: "str-enum[UnitLength]", }, ), frozen: false, }, ], model_name: "CenterOfMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92120d30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CenterOfMass", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6164b10, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61643f0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74c0eb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "center_of_mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74c0eb0, ), ], }, expected_repr: "'center_of_mass'", name: "literal['center_of_mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['center_of_mass']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCenterOfMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93593630, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCenterOfMass", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.center_of_mass.CenterOfMass, type: Literal['center_of_mass'] = 'center_of_mass') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CenterOfMass[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CenterOfMass, required=True), 'type': FieldInfo(annotation=Literal['center_of_mass'], required=False, default='center_of_mass')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['center_of_mass'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionClosePath(**data)[source][source]

The response to the ‘ClosePath’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.close_path.ClosePath'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['close_path']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>, 'config': {'title': 'OptionClosePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionClosePath'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionClosePath:94483161987264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.close_path.ClosePath'>, 'config': {'title': 'ClosePath'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.close_path.ClosePath'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.close_path.ClosePath'>>]}, 'ref': 'kittycad.models.close_path.ClosePath:94483141264528', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ClosePath', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'close_path', 'schema': {'expected': ['close_path'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionClosePath', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934eb8c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74c0db0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "close_path", }, expected_py: None, name: "literal['close_path']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92128490, ), serializer: Fields( GeneralFieldsSerializer { fields: { "face_id": SerField { key_py: Py( 0x00007f42c71fe6a0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ClosePath", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionClosePath", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionClosePath", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6327930, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6327960, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007f42c63278d0, ), path: LookupPath( [ S( "face_id", Py( 0x00007f42c6327900, ), ), ], ), }, name_py: Py( 0x00007f42c71fe6a0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ClosePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92128490, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ClosePath", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6327990, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63279c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74c0db0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "close_path": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74c0db0, ), ], }, expected_repr: "'close_path'", name: "literal['close_path']", }, ), validate_default: false, copy_default: false, name: "default[literal['close_path']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionClosePath", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934eb8c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionClosePath", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.close_path.ClosePath, type: Literal['close_path'] = 'close_path') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ClosePath[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ClosePath, required=True), 'type': FieldInfo(annotation=Literal['close_path'], required=False, default='close_path')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['close_path'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints(**data)[source][source]

The response to the ‘CurveGetControlPoints’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_control_points']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>, 'config': {'title': 'OptionCurveGetControlPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetControlPoints:94483162337584', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, 'config': {'title': 'CurveGetControlPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_control_points.CurveGetControlPoints'>>]}, 'ref': 'kittycad.models.curve_get_control_points.CurveGetControlPoints:94483141685872', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'control_points': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'CurveGetControlPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_control_points', 'schema': {'expected': ['curve_get_control_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetControlPoints', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93541130, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee9218f270, ), serializer: Fields( GeneralFieldsSerializer { fields: { "control_points": SerField { key_py: Py( 0x00007f42c71c2770, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055ee92bc80a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[Point3d]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CurveGetControlPoints", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73897f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_control_points", }, expected_py: None, name: "literal['curve_get_control_points']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetControlPoints", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetControlPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63ddfe0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63ddf50, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "control_points", lookup_key: Simple { key: "control_points", py_key: Py( 0x00007f42c6150a70, ), path: LookupPath( [ S( "control_points", Py( 0x00007f42c6150a30, ), ), ], ), }, name_py: Py( 0x00007f42c71c2770, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92bc80a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point3d", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "CurveGetControlPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9218f270, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CurveGetControlPoints", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de130, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63ddda0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73897f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_control_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73897f0, ), ], }, expected_repr: "'curve_get_control_points'", name: "literal['curve_get_control_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_control_points']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetControlPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93541130, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCurveGetControlPoints", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_control_points.CurveGetControlPoints, type: Literal['curve_get_control_points'] = 'curve_get_control_points') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CurveGetControlPoints[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetControlPoints, required=True), 'type': FieldInfo(annotation=Literal['curve_get_control_points'], required=False, default='curve_get_control_points')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['curve_get_control_points'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints(**data)[source][source]

The response to the ‘CurveGetEndPoints’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_end_points']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>, 'config': {'title': 'OptionCurveGetEndPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetEndPoints:94483162480784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, 'config': {'title': 'CurveGetEndPoints'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_end_points.CurveGetEndPoints'>>]}, 'ref': 'kittycad.models.curve_get_end_points.CurveGetEndPoints:94483141693600', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'end': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'start': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CurveGetEndPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_end_points', 'schema': {'expected': ['curve_get_end_points'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetEndPoints', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93564090, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee921910a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "start": SerField { key_py: Py( 0x00007f42cb03d1a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "end": SerField { key_py: Py( 0x00007f42cb039760, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "CurveGetEndPoints", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b02a70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_end_points", }, expected_py: None, name: "literal['curve_get_end_points']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetEndPoints", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetEndPoints", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dfd20, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63de970, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "end", lookup_key: Simple { key: "end", py_key: Py( 0x00007f42c63dfcf0, ), path: LookupPath( [ S( "end", Py( 0x00007f42c63dea90, ), ), ], ), }, name_py: Py( 0x00007f42cb039760, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "start", lookup_key: Simple { key: "start", py_key: Py( 0x00007f42c63dee80, ), path: LookupPath( [ S( "start", Py( 0x00007f42c63def70, ), ), ], ), }, name_py: Py( 0x00007f42cb03d1a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CurveGetEndPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee921910a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CurveGetEndPoints", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de550, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63de4c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b02a70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_end_points": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b02a70, ), ], }, expected_repr: "'curve_get_end_points'", name: "literal['curve_get_end_points']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_end_points']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetEndPoints", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93564090, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCurveGetEndPoints", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_end_points.CurveGetEndPoints, type: Literal['curve_get_end_points'] = 'curve_get_end_points') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CurveGetEndPoints[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetEndPoints, required=True), 'type': FieldInfo(annotation=Literal['curve_get_end_points'], required=False, default='curve_get_end_points')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['curve_get_end_points'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionCurveGetType(**data)[source][source]

The response to the ‘CurveGetType’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.curve_get_type.CurveGetType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['curve_get_type']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>, 'config': {'title': 'OptionCurveGetType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionCurveGetType:94483162351024', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.curve_get_type.CurveGetType'>, 'config': {'title': 'CurveGetType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.curve_get_type.CurveGetType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.curve_get_type.CurveGetType'>>]}, 'ref': 'kittycad.models.curve_get_type.CurveGetType:94483141681344', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'CurveType'>, 'members': [CurveType.LINE, CurveType.ARC, CurveType.NURBS], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.curve_type.CurveType:94483141679568', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'CurveGetType', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'curve_get_type', 'schema': {'expected': ['curve_get_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionCurveGetType', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935445b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee9218e0c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_type": SerField { key_py: Py( 0x00007f42c7b029f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee9218d9d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "CurveGetType", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b02b70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "curve_get_type", }, expected_py: None, name: "literal['curve_get_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionCurveGetType", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionCurveGetType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dcf00, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dd1a0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_type", lookup_key: Simple { key: "curve_type", py_key: Py( 0x00007f42c64708f0, ), path: LookupPath( [ S( "curve_type", Py( 0x00007f42c6151b70, ), ), ], ), }, name_py: Py( 0x00007f42c7b029f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee9218d9d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "arc": 1, "nurbs": 2, "line": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c68b0f50, ), Py( 0x00007f42c68b0fb0, ), Py( 0x00007f42c68b1010, ), ], }, missing: None, expected_repr: "'line', 'arc' or 'nurbs'", strict: false, class_repr: "CurveType", name: "str-enum[CurveType]", }, ), frozen: false, }, ], model_name: "CurveGetType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9218e0c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CurveGetType", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dcea0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dd350, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b02b70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "curve_get_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b02b70, ), ], }, expected_repr: "'curve_get_type'", name: "literal['curve_get_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['curve_get_type']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionCurveGetType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935445b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionCurveGetType", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.curve_get_type.CurveGetType, type: Literal['curve_get_type'] = 'curve_get_type') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: CurveGetType[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=CurveGetType, required=True), 'type': FieldInfo(annotation=Literal['curve_get_type'], required=False, default='curve_get_type')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['curve_get_type'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn(**data)[source][source]

The response to the ‘DefaultCameraFocusOn’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_focus_on']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>, 'config': {'title': 'OptionDefaultCameraFocusOn'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraFocusOn:94483162242016', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, 'config': {'title': 'DefaultCameraFocusOn'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn'>>]}, 'ref': 'kittycad.models.default_camera_focus_on.DefaultCameraFocusOn:94483142130640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {}, 'model_name': 'DefaultCameraFocusOn', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_focus_on', 'schema': {'expected': ['default_camera_focus_on'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraFocusOn', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93529be0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee921fbbd0, ), serializer: Fields( GeneralFieldsSerializer { fields: {}, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 0, }, ), has_extra: false, root_model: false, name: "DefaultCameraFocusOn", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b015f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_focus_on", }, expected_py: None, name: "literal['default_camera_focus_on']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraFocusOn", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraFocusOn", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dd740, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dd770, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [], model_name: "DefaultCameraFocusOn", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee921fbbd0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "DefaultCameraFocusOn", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dd7a0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dd7d0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b015f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_focus_on": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b015f0, ), ], }, expected_repr: "'default_camera_focus_on'", name: "literal['default_camera_focus_on']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_focus_on']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraFocusOn", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93529be0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionDefaultCameraFocusOn", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_focus_on.DefaultCameraFocusOn, type: Literal['default_camera_focus_on'] = 'default_camera_focus_on') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: DefaultCameraFocusOn[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraFocusOn, required=True), 'type': FieldInfo(annotation=Literal['default_camera_focus_on'], required=False, default='default_camera_focus_on')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['default_camera_focus_on'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings(**data)[source][source]

The response to the ‘DefaultCameraGetSettings’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_get_settings']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>, 'config': {'title': 'OptionDefaultCameraGetSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraGetSettings:94483162047360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, 'config': {'title': 'DefaultCameraGetSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings'>>]}, 'ref': 'kittycad.models.default_camera_get_settings.DefaultCameraGetSettings:94483142123664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'DefaultCameraGetSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_get_settings', 'schema': {'expected': ['default_camera_get_settings'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraGetSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934fa380, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee921fa090, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "DefaultCameraGetSettings", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7389840, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_get_settings", }, expected_py: None, name: "literal['default_camera_get_settings']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraGetSettings", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraGetSettings", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dc120, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dc150, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c63d12b0, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c63d12f0, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c6327f00, ), path: LookupPath( [ S( "center", Py( 0x00007f42c6327f30, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c6327f60, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c6327f90, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c671c0f0, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c63d03b0, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c6327fc0, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c63dc030, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c63d11f0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c63d1df0, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c63dc060, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c63dc090, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c63dc0c0, ), path: LookupPath( [ S( "up", Py( 0x00007f42c63dc0f0, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "DefaultCameraGetSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee921fa090, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "DefaultCameraGetSettings", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dc180, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dc1b0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7389840, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_get_settings": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7389840, ), ], }, expected_repr: "'default_camera_get_settings'", name: "literal['default_camera_get_settings']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_get_settings']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraGetSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934fa380, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionDefaultCameraGetSettings", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_get_settings.DefaultCameraGetSettings, type: Literal['default_camera_get_settings'] = 'default_camera_get_settings') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: DefaultCameraGetSettings[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraGetSettings, required=True), 'type': FieldInfo(annotation=Literal['default_camera_get_settings'], required=False, default='default_camera_get_settings')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['default_camera_get_settings'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom(**data)[source][source]

The response to the ‘DefaultCameraZoom’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['default_camera_zoom']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>, 'config': {'title': 'OptionDefaultCameraZoom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDefaultCameraZoom:94483162084608', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, 'config': {'title': 'DefaultCameraZoom'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.default_camera_zoom.DefaultCameraZoom'>>]}, 'ref': 'kittycad.models.default_camera_zoom.DefaultCameraZoom:94483155378736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'DefaultCameraZoom', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'default_camera_zoom', 'schema': {'expected': ['default_camera_zoom'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDefaultCameraZoom', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93503500, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92e9e230, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "DefaultCameraZoom", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b011b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "default_camera_zoom", }, expected_py: None, name: "literal['default_camera_zoom']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDefaultCameraZoom", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDefaultCameraZoom", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63254d0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6325680, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c63741b0, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c63770f0, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c6327e70, ), path: LookupPath( [ S( "center", Py( 0x00007f42c6327c30, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c6327e10, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c6326bb0, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c6376f30, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c63745f0, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c6327ea0, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c63255f0, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c6377cb0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c6377e30, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c6325320, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c6325380, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c6326d90, ), path: LookupPath( [ S( "up", Py( 0x00007f42c63256b0, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "DefaultCameraZoom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92e9e230, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "DefaultCameraZoom", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6325560, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63270c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b011b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "default_camera_zoom": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b011b0, ), ], }, expected_repr: "'default_camera_zoom'", name: "literal['default_camera_zoom']", }, ), validate_default: false, copy_default: false, name: "default[literal['default_camera_zoom']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionDefaultCameraZoom", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93503500, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionDefaultCameraZoom", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.default_camera_zoom.DefaultCameraZoom, type: Literal['default_camera_zoom'] = 'default_camera_zoom') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: DefaultCameraZoom[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=DefaultCameraZoom, required=True), 'type': FieldInfo(annotation=Literal['default_camera_zoom'], required=False, default='default_camera_zoom')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['default_camera_zoom'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionDensity(**data)[source][source]

The response to the ‘Density’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.density.Density'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['density']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>, 'config': {'title': 'OptionDensity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionDensity'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionDensity:94483162647792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.density.Density'>, 'config': {'title': 'Density'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.density.Density'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.density.Density'>>]}, 'ref': 'kittycad.models.density.Density:94483155409984', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'density': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitDensity'>, 'members': [UnitDensity.LB_FT3, UnitDensity.KG_M3], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_density.UnitDensity:94483150322848', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'Density', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'density', 'schema': {'expected': ['density'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionDensity', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9358ccf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ea5c40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "density": SerField { key_py: Py( 0x00007f42c822e160, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007f42c71c2f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee929cbca0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Density", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c822e160, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "density", }, expected_py: None, name: "literal['density']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionDensity", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionDensity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6166ee0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6166f10, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "density", lookup_key: Simple { key: "density", py_key: Py( 0x00007f42c6166e80, ), path: LookupPath( [ S( "density", Py( 0x00007f42c6166eb0, ), ), ], ), }, name_py: Py( 0x00007f42c822e160, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f42c6186b30, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f42c6186af0, ), ), ], ), }, name_py: Py( 0x00007f42c71c2f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee929cbca0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "kg:m3": 1, "lb:ft3": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c69cd790, ), Py( 0x00007f42c69cd7f0, ), ], }, missing: None, expected_repr: "'lb:ft3' or 'kg:m3'", strict: false, class_repr: "UnitDensity", name: "str-enum[UnitDensity]", }, ), frozen: false, }, ], model_name: "Density", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ea5c40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Density", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6166f40, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6166f70, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c822e160, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "density": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c822e160, ), ], }, expected_repr: "'density'", name: "literal['density']", }, ), validate_default: false, copy_default: false, name: "default[literal['density']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionDensity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9358ccf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionDensity", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.density.Density, type: Literal['density'] = 'density') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Density[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Density, required=True), 'type': FieldInfo(annotation=Literal['density'], required=False, default='density')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['density'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEmpty(**data)[source][source]

An empty response, used for any command that does not explicitly have a response defined here.

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['empty']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>, 'config': {'title': 'OptionEmpty'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEmpty'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEmpty:94483161802224', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'empty', 'schema': {'expected': ['empty'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEmpty', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934be5f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42cafa9f40, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "empty", }, expected_py: None, name: "literal['empty']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "OptionEmpty", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEmpty", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63a75a0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63a7330, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42cafa9f40, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "empty": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42cafa9f40, ), ], }, expected_repr: "'empty'", name: "literal['empty']", }, ), validate_default: false, copy_default: false, name: "default[literal['empty']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEmpty", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934be5f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEmpty", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, type: Literal['empty'] = 'empty') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'type': FieldInfo(annotation=Literal['empty'], required=False, default='empty')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['empty'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern(**data)[source][source]

The response to the ‘EntityCircularPattern’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_circular_pattern']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>, 'config': {'title': 'OptionEntityCircularPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityCircularPattern:94483162763072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, 'config': {'title': 'EntityCircularPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_circular_pattern.EntityCircularPattern'>>]}, 'ref': 'kittycad.models.entity_circular_pattern.EntityCircularPattern:94483155537296', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityCircularPattern', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_circular_pattern', 'schema': {'expected': ['entity_circular_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityCircularPattern', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935a8f40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b03df0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_circular_pattern", }, expected_py: None, name: "literal['entity_circular_pattern']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ec4d90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityCircularPattern", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityCircularPattern", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityCircularPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61672a0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6165bf0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c71a5ff0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c6190930, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityCircularPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ec4d90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityCircularPattern", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6165c80, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6165620, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b03df0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_circular_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b03df0, ), ], }, expected_repr: "'entity_circular_pattern'", name: "literal['entity_circular_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_circular_pattern']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityCircularPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935a8f40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityCircularPattern", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_circular_pattern.EntityCircularPattern, type: Literal['entity_circular_pattern'] = 'entity_circular_pattern') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityCircularPattern[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityCircularPattern, required=True), 'type': FieldInfo(annotation=Literal['entity_circular_pattern'], required=False, default='entity_circular_pattern')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_circular_pattern'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids(**data)[source][source]

The response to the ‘EntityGetAllChildUuids’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_all_child_uuids']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>, 'config': {'title': 'OptionEntityGetAllChildUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetAllChildUuids:94483161951824', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, 'config': {'title': 'EntityGetAllChildUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids'>>]}, 'ref': 'kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids:94483155545248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityGetAllChildUuids', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_all_child_uuids', 'schema': {'expected': ['entity_get_all_child_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetAllChildUuids', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934e2e50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ec6ca0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetAllChildUuids", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7389e80, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_all_child_uuids", }, expected_py: None, name: "literal['entity_get_all_child_uuids']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetAllChildUuids", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetAllChildUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6324e10, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6324e70, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c640d930, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c640dbb0, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityGetAllChildUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ec6ca0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetAllChildUuids", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6324e40, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6324f90, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7389e80, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_all_child_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7389e80, ), ], }, expected_repr: "'entity_get_all_child_uuids'", name: "literal['entity_get_all_child_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_all_child_uuids']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetAllChildUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934e2e50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetAllChildUuids", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_all_child_uuids.EntityGetAllChildUuids, type: Literal['entity_get_all_child_uuids'] = 'entity_get_all_child_uuids') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetAllChildUuids[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetAllChildUuids, required=True), 'type': FieldInfo(annotation=Literal['entity_get_all_child_uuids'], required=False, default='entity_get_all_child_uuids')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_all_child_uuids'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid(**data)[source][source]

The response to the ‘EntityGetChildUuid’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_child_uuid']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>, 'config': {'title': 'OptionEntityGetChildUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetChildUuid:94483161914960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, 'config': {'title': 'EntityGetChildUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid'>>]}, 'ref': 'kittycad.models.entity_get_child_uuid.EntityGetChildUuid:94483155551264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'EntityGetChildUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_child_uuid', 'schema': {'expected': ['entity_get_child_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetChildUuid', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934d9e50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b025b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_child_uuid", }, expected_py: None, name: "literal['entity_get_child_uuid']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ec8420, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007f42c67ea670, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetChildUuid", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetChildUuid", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetChildUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6325710, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6325740, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007f42c64be4f0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007f42c63da370, ), ), ], ), }, name_py: Py( 0x00007f42c67ea670, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "EntityGetChildUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ec8420, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetChildUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6325770, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63257a0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b025b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_child_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b025b0, ), ], }, expected_repr: "'entity_get_child_uuid'", name: "literal['entity_get_child_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_child_uuid']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetChildUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934d9e50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetChildUuid", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_child_uuid.EntityGetChildUuid, type: Literal['entity_get_child_uuid'] = 'entity_get_child_uuid') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetChildUuid[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetChildUuid, required=True), 'type': FieldInfo(annotation=Literal['entity_get_child_uuid'], required=False, default='entity_get_child_uuid')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_child_uuid'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance(**data)[source][source]

The response to the ‘EntityGetDistance’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_distance.EntityGetDistance'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_distance']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>, 'config': {'title': 'OptionEntityGetDistance'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetDistance:94483157213392', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_distance.EntityGetDistance'>, 'config': {'title': 'EntityGetDistance'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_distance.EntityGetDistance'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_distance.EntityGetDistance'>>]}, 'ref': 'kittycad.models.entity_get_distance.EntityGetDistance:94483155571360', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'max_distance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'min_distance': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'EntityGetDistance', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_distance', 'schema': {'expected': ['entity_get_distance'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetDistance', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9305e0d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ecd2a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "max_distance": SerField { key_py: Py( 0x00007f42c9ed9db0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "min_distance": SerField { key_py: Py( 0x00007f42c67eb470, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "EntityGetDistance", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b02cb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_distance", }, expected_py: None, name: "literal['entity_get_distance']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetDistance", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetDistance", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6167570, ), path: LookupPath( [ S( "data", Py( 0x00007f42c61675a0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "max_distance", lookup_key: Simple { key: "max_distance", py_key: Py( 0x00007f42c65810b0, ), path: LookupPath( [ S( "max_distance", Py( 0x00007f42c6192cf0, ), ), ], ), }, name_py: Py( 0x00007f42c9ed9db0, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055ee92ecce10, ), config: Py( 0x00007f42c6192ac0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "min_distance", lookup_key: Simple { key: "min_distance", py_key: Py( 0x00007f42c6192d70, ), path: LookupPath( [ S( "min_distance", Py( 0x00007f42c6192d30, ), ), ], ), }, name_py: Py( 0x00007f42c67eb470, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055ee92ecce10, ), config: Py( 0x00007f42c6192ac0, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "EntityGetDistance", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ecd2a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetDistance", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61675d0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6167600, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b02cb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_distance": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b02cb0, ), ], }, expected_repr: "'entity_get_distance'", name: "literal['entity_get_distance']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_distance']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetDistance", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9305e0d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetDistance", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_distance.EntityGetDistance, type: Literal['entity_get_distance'] = 'entity_get_distance') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetDistance[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetDistance, required=True), 'type': FieldInfo(annotation=Literal['entity_get_distance'], required=False, default='entity_get_distance')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_distance'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren(**data)[source][source]

The response to the ‘EntityGetNumChildren’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_num_children']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>, 'config': {'title': 'OptionEntityGetNumChildren'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetNumChildren:94483161930336', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, 'config': {'title': 'EntityGetNumChildren'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_num_children.EntityGetNumChildren'>>]}, 'ref': 'kittycad.models.entity_get_num_children.EntityGetNumChildren:94483155558944', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'num': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'EntityGetNumChildren', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_num_children', 'schema': {'expected': ['entity_get_num_children'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetNumChildren', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934dda60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92eca220, ), serializer: Fields( GeneralFieldsSerializer { fields: { "num": SerField { key_py: Py( 0x00007f42ca1335d0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetNumChildren", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b01e70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_num_children", }, expected_py: None, name: "literal['entity_get_num_children']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetNumChildren", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetNumChildren", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63a7db0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63a7b40, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "num", lookup_key: Simple { key: "num", py_key: Py( 0x00007f42c635b4b0, ), path: LookupPath( [ S( "num", Py( 0x00007f42c635b960, ), ), ], ), }, name_py: Py( 0x00007f42ca1335d0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "EntityGetNumChildren", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92eca220, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetNumChildren", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63a7b10, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63a7d80, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b01e70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_num_children": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b01e70, ), ], }, expected_repr: "'entity_get_num_children'", name: "literal['entity_get_num_children']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_num_children']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetNumChildren", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934dda60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetNumChildren", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_num_children.EntityGetNumChildren, type: Literal['entity_get_num_children'] = 'entity_get_num_children') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetNumChildren[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetNumChildren, required=True), 'type': FieldInfo(annotation=Literal['entity_get_num_children'], required=False, default='entity_get_num_children')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_num_children'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId(**data)[source][source]

The response to the ‘EntityGetParentId’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_parent_id']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>, 'config': {'title': 'OptionEntityGetParentId'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetParentId:94483161941008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, 'config': {'title': 'EntityGetParentId'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_parent_id.EntityGetParentId'>>]}, 'ref': 'kittycad.models.entity_get_parent_id.EntityGetParentId:94483155563936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'EntityGetParentId', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_parent_id', 'schema': {'expected': ['entity_get_parent_id'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetParentId', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934e0410, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b014b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_parent_id", }, expected_py: None, name: "literal['entity_get_parent_id']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ecb5a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007f42c67ea670, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetParentId", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetParentId", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetParentId", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6325890, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6325860, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007f42c63db4f0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007f42c63dacb0, ), ), ], ), }, name_py: Py( 0x00007f42c67ea670, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "EntityGetParentId", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ecb5a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetParentId", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63260d0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6325980, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b014b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_parent_id": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b014b0, ), ], }, expected_repr: "'entity_get_parent_id'", name: "literal['entity_get_parent_id']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_parent_id']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetParentId", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934e0410, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetParentId", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_parent_id.EntityGetParentId, type: Literal['entity_get_parent_id'] = 'entity_get_parent_id') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetParentId[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetParentId, required=True), 'type': FieldInfo(annotation=Literal['entity_get_parent_id'], required=False, default='entity_get_parent_id')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_parent_id'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths(**data)[source][source]

The response to the ‘EntityGetSketchPaths’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_get_sketch_paths']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>, 'config': {'title': 'OptionEntityGetSketchPaths'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityGetSketchPaths:94483161962592', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, 'config': {'title': 'EntityGetSketchPaths'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths'>>]}, 'ref': 'kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths:94483155590272', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityGetSketchPaths', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_get_sketch_paths', 'schema': {'expected': ['entity_get_sketch_paths'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityGetSketchPaths', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934e5860, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ed1c80, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityGetSketchPaths", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b03b30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_get_sketch_paths", }, expected_py: None, name: "literal['entity_get_sketch_paths']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityGetSketchPaths", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityGetSketchPaths", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6326e80, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6326eb0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c66ca370, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c640f570, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityGetSketchPaths", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ed1c80, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityGetSketchPaths", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6326ee0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6326f10, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b03b30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_get_sketch_paths": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b03b30, ), ], }, expected_repr: "'entity_get_sketch_paths'", name: "literal['entity_get_sketch_paths']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_get_sketch_paths']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityGetSketchPaths", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934e5860, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityGetSketchPaths", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_get_sketch_paths.EntityGetSketchPaths, type: Literal['entity_get_sketch_paths'] = 'entity_get_sketch_paths') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityGetSketchPaths[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityGetSketchPaths, required=True), 'type': FieldInfo(annotation=Literal['entity_get_sketch_paths'], required=False, default='entity_get_sketch_paths')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_get_sketch_paths'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern(**data)[source][source]

The response to the ‘EntityLinearPattern’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_linear_pattern']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>, 'config': {'title': 'OptionEntityLinearPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPattern:94483162750784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, 'config': {'title': 'EntityLinearPattern'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_linear_pattern.EntityLinearPattern'>>]}, 'ref': 'kittycad.models.entity_linear_pattern.EntityLinearPattern:94483155596544', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityLinearPattern', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern', 'schema': {'expected': ['entity_linear_pattern'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPattern', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935a5f40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ed3500, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityLinearPattern", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7b011f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern", }, expected_py: None, name: "literal['entity_linear_pattern']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPattern", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPattern", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6167960, ), path: LookupPath( [ S( "data", Py( 0x00007f42c61679f0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c61850b0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c6193af0, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityLinearPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ed3500, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityLinearPattern", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61678a0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61678d0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7b011f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7b011f0, ), ], }, expected_repr: "'entity_linear_pattern'", name: "literal['entity_linear_pattern']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPattern", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935a5f40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityLinearPattern", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_linear_pattern.EntityLinearPattern, type: Literal['entity_linear_pattern'] = 'entity_linear_pattern') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityLinearPattern[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityLinearPattern, required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern'], required=False, default='entity_linear_pattern')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_linear_pattern'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform(**data)[source][source]

The response to the ‘EntityLinearPatternTransform’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['entity_linear_pattern_transform']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>, 'config': {'title': 'OptionEntityLinearPatternTransform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionEntityLinearPatternTransform:94483162737392', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, 'config': {'title': 'EntityLinearPatternTransform'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform'>>]}, 'ref': 'kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform:94483155602112', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'EntityLinearPatternTransform', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'entity_linear_pattern_transform', 'schema': {'expected': ['entity_linear_pattern_transform'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionEntityLinearPatternTransform', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935a2af0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c7389ed0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "entity_linear_pattern_transform", }, expected_py: None, name: "literal['entity_linear_pattern_transform']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ed4ac0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "EntityLinearPatternTransform", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionEntityLinearPatternTransform", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionEntityLinearPatternTransform", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6167a80, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6167ab0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c6198070, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c6198030, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "EntityLinearPatternTransform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ed4ac0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "EntityLinearPatternTransform", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6167ae0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6167b10, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c7389ed0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity_linear_pattern_transform": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c7389ed0, ), ], }, expected_repr: "'entity_linear_pattern_transform'", name: "literal['entity_linear_pattern_transform']", }, ), validate_default: false, copy_default: false, name: "default[literal['entity_linear_pattern_transform']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionEntityLinearPatternTransform", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935a2af0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionEntityLinearPatternTransform", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.entity_linear_pattern_transform.EntityLinearPatternTransform, type: Literal['entity_linear_pattern_transform'] = 'entity_linear_pattern_transform') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: EntityLinearPatternTransform[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=EntityLinearPatternTransform, required=True), 'type': FieldInfo(annotation=Literal['entity_linear_pattern_transform'], required=False, default='entity_linear_pattern_transform')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['entity_linear_pattern_transform'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionExport(**data)[source][source]

The response to the ‘Export’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.export.Export'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['export']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>, 'config': {'title': 'OptionExport'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExport'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExport:94483161803216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.export.Export'>, 'config': {'title': 'Export'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.export.Export'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.export.Export'>>]}, 'ref': 'kittycad.models.export.Export:94483155679888', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'files': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.export_file.ExportFile'>, 'config': {'title': 'ExportFile'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.export_file.ExportFile'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.export_file.ExportFile'>>]}, 'ref': 'kittycad.models.export_file.ExportFile:94483155670960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'contents': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <bound method Base64Data.validate of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'no-info'}, 'schema': {'choices': [{'type': 'str'}, {'type': 'bytes'}], 'type': 'union'}, 'serialization': {'function': <bound method Base64Data.serialize of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'function-plain'}, 'type': 'function-after'}, 'type': 'model-field'}, 'name': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ExportFile', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Export', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'export', 'schema': {'expected': ['export'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExport', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934be9d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c97b0570, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "export", }, expected_py: None, name: "literal['export']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92ee7a90, ), serializer: Fields( GeneralFieldsSerializer { fields: { "files": SerField { key_py: Py( 0x00007f42caff1b60, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055ee92ee57b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "contents": SerField { key_py: Py( 0x00007f42caff5940, ), alias: None, alias_py: None, serializer: Some( Function( FunctionPlainSerializer { func: Py( 0x00007f42c680c900, ), name: "plain_function[serialize]", function_name: "serialize", return_serializer: Any( AnySerializer, ), fallback_serializer: None, when_used: Always, is_field_serializer: false, info_arg: false, }, ), ), required: true, }, "name": SerField { key_py: Py( 0x00007f42cb03b7b0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "ExportFile", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[ExportFile]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Export", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExport", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExport", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6324ab0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6324bd0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "files", lookup_key: Simple { key: "files", py_key: Py( 0x00007f42c6324390, ), path: LookupPath( [ S( "files", Py( 0x00007f42c6324330, ), ), ], ), }, name_py: Py( 0x00007f42caff1b60, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "contents", lookup_key: Simple { key: "contents", py_key: Py( 0x00007f42c663e270, ), path: LookupPath( [ S( "contents", Py( 0x00007f42c63835f0, ), ), ], ), }, name_py: Py( 0x00007f42caff5940, ), validator: FunctionAfter( FunctionAfterValidator { validator: Union( UnionValidator { mode: Smart, choices: [ ( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), None, ), ( Bytes( BytesValidator { strict: false, bytes_mode: ValBytesMode { ser: Utf8, }, }, ), None, ), ], custom_error: None, strict: false, name: "union[str,bytes]", }, ), func: Py( 0x00007f42c680c8c0, ), config: Py( 0x00007f42c6383280, ), name: "function-after[validate(), union[str,bytes]]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "name", lookup_key: Simple { key: "name", py_key: Py( 0x00007f42c63248d0, ), path: LookupPath( [ S( "name", Py( 0x00007f42c6324360, ), ), ], ), }, name_py: Py( 0x00007f42cb03b7b0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ExportFile", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ee57b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ExportFile", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Export", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92ee7a90, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Export", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6324990, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6324930, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c97b0570, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "export": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c97b0570, ), ], }, expected_repr: "'export'", name: "literal['export']", }, ), validate_default: false, copy_default: false, name: "default[literal['export']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionExport", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934be9d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionExport", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.export.Export, type: Literal['export'] = 'export') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Export[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Export, required=True), 'type': FieldInfo(annotation=Literal['export'], required=False, default='export')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['export'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo(**data)[source][source]

The response to the ‘ExtrusionFaceInfo’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['extrusion_face_info']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>, 'config': {'title': 'OptionExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionExtrusionFaceInfo:94483162787504', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'config': {'title': 'ExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo:94483155814256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cap': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ExtrusionFaceCapType'>, 'members': [ExtrusionFaceCapType.NONE, ExtrusionFaceCapType.TOP, ExtrusionFaceCapType.BOTTOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.extrusion_face_cap_type.ExtrusionFaceCapType:94483155810096', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'extrusion_face_info', 'schema': {'expected': ['extrusion_face_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935aeeb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e0030, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "extrusion_face_info", }, expected_py: None, name: "literal['extrusion_face_info']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f08770, ), serializer: Fields( GeneralFieldsSerializer { fields: { "cap": SerField { key_py: Py( 0x00007f42c8eb61f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee92f07730, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "curve_id": SerField { key_py: Py( 0x00007f42c6644eb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "face_id": SerField { key_py: Py( 0x00007f42c71fe6a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "ExtrusionFaceInfo", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionExtrusionFaceInfo", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionExtrusionFaceInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61a0420, ), path: LookupPath( [ S( "data", Py( 0x00007f42c61a0450, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cap", lookup_key: Simple { key: "cap", py_key: Py( 0x00007f42c61a0360, ), path: LookupPath( [ S( "cap", Py( 0x00007f42c61a0390, ), ), ], ), }, name_py: Py( 0x00007f42c8eb61f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee92f07730, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "none": 0, "bottom": 2, "top": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c67f5e50, ), Py( 0x00007f42c67f5eb0, ), Py( 0x00007f42c67f5f10, ), ], }, missing: None, expected_repr: "'none', 'top' or 'bottom'", strict: false, class_repr: "ExtrusionFaceCapType", name: "str-enum[ExtrusionFaceCapType]", }, ), frozen: false, }, Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007f42c619be30, ), path: LookupPath( [ S( "curve_id", Py( 0x00007f42c619bdf0, ), ), ], ), }, name_py: Py( 0x00007f42c6644eb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007f42c61a03c0, ), path: LookupPath( [ S( "face_id", Py( 0x00007f42c61a03f0, ), ), ], ), }, name_py: Py( 0x00007f42c71fe6a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "ExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f08770, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ExtrusionFaceInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61a0480, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61a04b0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e0030, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "extrusion_face_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e0030, ), ], }, expected_repr: "'extrusion_face_info'", name: "literal['extrusion_face_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['extrusion_face_info']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935aeeb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionExtrusionFaceInfo", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.extrusion_face_info.ExtrusionFaceInfo, type: Literal['extrusion_face_info'] = 'extrusion_face_info') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ExtrusionFaceInfo[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ExtrusionFaceInfo, required=True), 'type': FieldInfo(annotation=Literal['extrusion_face_info'], required=False, default='extrusion_face_info')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['extrusion_face_info'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter(**data)[source][source]

The response to the ‘FaceGetCenter’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_center.FaceGetCenter'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_center']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>, 'config': {'title': 'OptionFaceGetCenter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetCenter:94483162541072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_center.FaceGetCenter'>, 'config': {'title': 'FaceGetCenter'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_center.FaceGetCenter'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_center.FaceGetCenter'>>]}, 'ref': 'kittycad.models.face_get_center.FaceGetCenter:94483155837408', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'FaceGetCenter', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_center', 'schema': {'expected': ['face_get_center'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetCenter', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93572c10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f0e1e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92bc80a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "FaceGetCenter", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e1030, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_center", }, expected_py: None, name: "literal['face_get_center']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetCenter", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetCenter", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6165290, ), path: LookupPath( [ S( "data", Py( 0x00007f42c61652c0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c6165230, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c6165260, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92bc80a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point3d", }, ), frozen: false, }, ], model_name: "FaceGetCenter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f0e1e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "FaceGetCenter", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61652f0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6165320, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e1030, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_center": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e1030, ), ], }, expected_repr: "'face_get_center'", name: "literal['face_get_center']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_center']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetCenter", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93572c10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionFaceGetCenter", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_center.FaceGetCenter, type: Literal['face_get_center'] = 'face_get_center') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: FaceGetCenter[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetCenter, required=True), 'type': FieldInfo(annotation=Literal['face_get_center'], required=False, default='face_get_center')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['face_get_center'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient(**data)[source][source]

The response to the ‘FaceGetGradient’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_gradient.FaceGetGradient'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_gradient']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>, 'config': {'title': 'OptionFaceGetGradient'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetGradient:94483162556928', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_gradient.FaceGetGradient'>, 'config': {'title': 'FaceGetGradient'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_gradient.FaceGetGradient'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_gradient.FaceGetGradient'>>]}, 'ref': 'kittycad.models.face_get_gradient.FaceGetGradient:94483155846272', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'df_du': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'df_dv': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'normal': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'FaceGetGradient', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_gradient', 'schema': {'expected': ['face_get_gradient'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetGradient', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93576a00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e0ff0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_gradient", }, expected_py: None, name: "literal['face_get_gradient']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f10480, ), serializer: Fields( GeneralFieldsSerializer { fields: { "df_du": SerField { key_py: Py( 0x00007f42c6809cb0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "normal": SerField { key_py: Py( 0x00007f42c97bed90, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "df_dv": SerField { key_py: Py( 0x00007f42c6809d40, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "FaceGetGradient", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetGradient", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetGradient", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61658f0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6165920, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "df_du", lookup_key: Simple { key: "df_du", py_key: Py( 0x00007f42c61657d0, ), path: LookupPath( [ S( "df_du", Py( 0x00007f42c6165800, ), ), ], ), }, name_py: Py( 0x00007f42c6809cb0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "df_dv", lookup_key: Simple { key: "df_dv", py_key: Py( 0x00007f42c6165830, ), path: LookupPath( [ S( "df_dv", Py( 0x00007f42c6165860, ), ), ], ), }, name_py: Py( 0x00007f42c6809d40, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "normal", lookup_key: Simple { key: "normal", py_key: Py( 0x00007f42c6165890, ), path: LookupPath( [ S( "normal", Py( 0x00007f42c61658c0, ), ), ], ), }, name_py: Py( 0x00007f42c97bed90, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "FaceGetGradient", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f10480, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "FaceGetGradient", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6165950, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6165980, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e0ff0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_gradient": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e0ff0, ), ], }, expected_repr: "'face_get_gradient'", name: "literal['face_get_gradient']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_gradient']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetGradient", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93576a00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionFaceGetGradient", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_gradient.FaceGetGradient, type: Literal['face_get_gradient'] = 'face_get_gradient') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: FaceGetGradient[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetGradient, required=True), 'type': FieldInfo(annotation=Literal['face_get_gradient'], required=False, default='face_get_gradient')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['face_get_gradient'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition(**data)[source][source]

The response to the ‘FaceGetPosition’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_get_position.FaceGetPosition'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_get_position']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>, 'config': {'title': 'OptionFaceGetPosition'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceGetPosition:94483162512496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_get_position.FaceGetPosition'>, 'config': {'title': 'FaceGetPosition'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_get_position.FaceGetPosition'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_get_position.FaceGetPosition'>>]}, 'ref': 'kittycad.models.face_get_position.FaceGetPosition:94483155860160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'FaceGetPosition', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_get_position', 'schema': {'expected': ['face_get_position'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceGetPosition', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9356bc70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f13ac0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92bc80a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "Point3d", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "FaceGetPosition", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e11b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_get_position", }, expected_py: None, name: "literal['face_get_position']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceGetPosition", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceGetPosition", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6164cf0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6164d20, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c6164c60, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c6164c30, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92bc80a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point3d", }, ), frozen: false, }, ], model_name: "FaceGetPosition", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f13ac0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "FaceGetPosition", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6164d50, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6164d80, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e11b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_get_position": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e11b0, ), ], }, expected_repr: "'face_get_position'", name: "literal['face_get_position']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_get_position']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionFaceGetPosition", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9356bc70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionFaceGetPosition", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_get_position.FaceGetPosition, type: Literal['face_get_position'] = 'face_get_position') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: FaceGetPosition[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceGetPosition, required=True), 'type': FieldInfo(annotation=Literal['face_get_position'], required=False, default='face_get_position')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['face_get_position'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar(**data)[source][source]

The response to the ‘FaceIsPlanar’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.face_is_planar.FaceIsPlanar'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['face_is_planar']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>, 'config': {'title': 'OptionFaceIsPlanar'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionFaceIsPlanar:94483162494176', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.face_is_planar.FaceIsPlanar'>, 'config': {'title': 'FaceIsPlanar'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.face_is_planar.FaceIsPlanar'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.face_is_planar.FaceIsPlanar'>>]}, 'ref': 'kittycad.models.face_is_planar.FaceIsPlanar:94483155870688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'x_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'y_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'z_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'FaceIsPlanar', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'face_is_planar', 'schema': {'expected': ['face_is_planar'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionFaceIsPlanar', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935674e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f163e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "x_axis": SerField { key_py: Py( 0x00007f42c680a910, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "z_axis": SerField { key_py: Py( 0x00007f42c680aaf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "origin": SerField { key_py: Py( 0x00007f42cb03bec0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, "y_axis": SerField { key_py: Py( 0x00007f42c680aa00, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "FaceIsPlanar", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e1270, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "face_is_planar", }, expected_py: None, name: "literal['face_is_planar']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionFaceIsPlanar", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionFaceIsPlanar", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6164990, ), path: LookupPath( [ S( "data", Py( 0x00007f42c61646c0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007f42c63dfba0, ), path: LookupPath( [ S( "origin", Py( 0x00007f42c63df8a0, ), ), ], ), }, name_py: Py( 0x00007f42cb03bec0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "x_axis", lookup_key: Simple { key: "x_axis", py_key: Py( 0x00007f42c63df840, ), path: LookupPath( [ S( "x_axis", Py( 0x00007f42c63df960, ), ), ], ), }, name_py: Py( 0x00007f42c680a910, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "y_axis", lookup_key: Simple { key: "y_axis", py_key: Py( 0x00007f42c63df900, ), path: LookupPath( [ S( "y_axis", Py( 0x00007f42c63df990, ), ), ], ), }, name_py: Py( 0x00007f42c680aa00, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "z_axis", lookup_key: Simple { key: "z_axis", py_key: Py( 0x00007f42c63df9c0, ), path: LookupPath( [ S( "z_axis", Py( 0x00007f42c63df930, ), ), ], ), }, name_py: Py( 0x00007f42c680aaf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: DefinitionRef( DefinitionRefValidator { definition: "Point3d", }, ), name: "nullable[Point3d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point3d]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "FaceIsPlanar", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f163e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "FaceIsPlanar", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6164750, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6164600, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e1270, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "face_is_planar": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e1270, ), ], }, expected_repr: "'face_is_planar'", name: "literal['face_is_planar']", }, ), validate_default: false, copy_default: false, name: "default[literal['face_is_planar']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionFaceIsPlanar", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935674e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionFaceIsPlanar", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.face_is_planar.FaceIsPlanar, type: Literal['face_is_planar'] = 'face_is_planar') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: FaceIsPlanar[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=FaceIsPlanar, required=True), 'type': FieldInfo(annotation=Literal['face_is_planar'], required=False, default='face_is_planar')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['face_is_planar'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionGetEntityType(**data)[source][source]

The response to the ‘GetEntityType’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_entity_type.GetEntityType'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_entity_type']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>, 'config': {'title': 'OptionGetEntityType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetEntityType:94483162324320', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_entity_type.GetEntityType'>, 'config': {'title': 'GetEntityType'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_entity_type.GetEntityType'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_entity_type.GetEntityType'>>]}, 'ref': 'kittycad.models.get_entity_type.GetEntityType:94483156270624', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'EntityType'>, 'members': [EntityType.ENTITY, EntityType.OBJECT, EntityType.PATH, EntityType.CURVE, EntityType.SOLID2D, EntityType.SOLID3D, EntityType.EDGE, EntityType.FACE, EntityType.PLANE, EntityType.VERTEX], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.entity_type.EntityType:94483155609552', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'GetEntityType', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_entity_type', 'schema': {'expected': ['get_entity_type'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetEntityType', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9353dd60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f77e20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_type": SerField { key_py: Py( 0x00007f42c7b03830, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee92ed67d0, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "GetEntityType", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e3970, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_entity_type", }, expected_py: None, name: "literal['get_entity_type']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetEntityType", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetEntityType", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dea30, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dec10, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_type", lookup_key: Simple { key: "entity_type", py_key: Py( 0x00007f42c614f7f0, ), path: LookupPath( [ S( "entity_type", Py( 0x00007f42c614ed70, ), ), ], ), }, name_py: Py( 0x00007f42c7b03830, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee92ed67d0, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "entity": 0, "curve": 3, "path": 2, "edge": 6, "plane": 8, "face": 7, "solid3d": 5, "solid2d": 4, "object": 1, "vertex": 9, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c67f4e30, ), Py( 0x00007f42c67f4fb0, ), Py( 0x00007f42c67f5010, ), Py( 0x00007f42c67f5070, ), Py( 0x00007f42c67f50d0, ), Py( 0x00007f42c67f5130, ), Py( 0x00007f42c67f5190, ), Py( 0x00007f42c67f51f0, ), Py( 0x00007f42c67f5250, ), Py( 0x00007f42c67f52b0, ), ], }, missing: None, expected_repr: "'entity', 'object', 'path', 'curve', 'solid2d', 'solid3d', 'edge', 'face', 'plane' or 'vertex'", strict: false, class_repr: "EntityType", name: "str-enum[EntityType]", }, ), frozen: false, }, ], model_name: "GetEntityType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f77e20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "GetEntityType", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de880, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63de8e0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e3970, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_entity_type": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e3970, ), ], }, expected_repr: "'get_entity_type'", name: "literal['get_entity_type']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_entity_type']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionGetEntityType", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9353dd60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionGetEntityType", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_entity_type.GetEntityType, type: Literal['get_entity_type'] = 'get_entity_type') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: GetEntityType[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetEntityType, required=True), 'type': FieldInfo(annotation=Literal['get_entity_type'], required=False, default='get_entity_type')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['get_entity_type'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects(**data)[source][source]

The response to the ‘GetNumObjects’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_num_objects.GetNumObjects'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_num_objects']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>, 'config': {'title': 'OptionGetNumObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetNumObjects:94483162198688', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_num_objects.GetNumObjects'>, 'config': {'title': 'GetNumObjects'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_num_objects.GetNumObjects'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_num_objects.GetNumObjects'>>]}, 'ref': 'kittycad.models.get_num_objects.GetNumObjects:94483156279936', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'num_objects': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'int'}, 'type': 'model-field'}}, 'model_name': 'GetNumObjects', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_num_objects', 'schema': {'expected': ['get_num_objects'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetNumObjects', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9351f2a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f7a280, ), serializer: Fields( GeneralFieldsSerializer { fields: { "num_objects": SerField { key_py: Py( 0x00007f42c792d4f0, ), alias: None, alias_py: None, serializer: Some( Int( IntSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "GetNumObjects", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e38f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_num_objects", }, expected_py: None, name: "literal['get_num_objects']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetNumObjects", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetNumObjects", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dd230, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dd260, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "num_objects", lookup_key: Simple { key: "num_objects", py_key: Py( 0x00007f42c637f8f0, ), path: LookupPath( [ S( "num_objects", Py( 0x00007f42c637fef0, ), ), ], ), }, name_py: Py( 0x00007f42c792d4f0, ), validator: Int( IntValidator { strict: false, }, ), frozen: false, }, ], model_name: "GetNumObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f7a280, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "GetNumObjects", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dd290, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dd2c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e38f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_num_objects": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e38f0, ), ], }, expected_repr: "'get_num_objects'", name: "literal['get_num_objects']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_num_objects']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionGetNumObjects", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9351f2a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionGetNumObjects", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_num_objects.GetNumObjects, type: Literal['get_num_objects'] = 'get_num_objects') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: GetNumObjects[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetNumObjects, required=True), 'type': FieldInfo(annotation=Literal['get_num_objects'], required=False, default='get_num_objects')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['get_num_objects'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane(**data)[source][source]

The response to the ‘GetSketchModePlane’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['get_sketch_mode_plane']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>, 'config': {'title': 'OptionGetSketchModePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionGetSketchModePlane:94483162688560', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, 'config': {'title': 'GetSketchModePlane'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane'>>]}, 'ref': 'kittycad.models.get_sketch_mode_plane.GetSketchModePlane:94483156295072', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'origin': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'x_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'y_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'z_axis': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'GetSketchModePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'get_sketch_mode_plane', 'schema': {'expected': ['get_sketch_mode_plane'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionGetSketchModePlane', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93596c30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f7dda0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "origin": SerField { key_py: Py( 0x00007f42cb03bec0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "y_axis": SerField { key_py: Py( 0x00007f42c680aa00, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "z_axis": SerField { key_py: Py( 0x00007f42c680aaf0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "x_axis": SerField { key_py: Py( 0x00007f42c680a910, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "GetSketchModePlane", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e3830, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "get_sketch_mode_plane", }, expected_py: None, name: "literal['get_sketch_mode_plane']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionGetSketchModePlane", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionGetSketchModePlane", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6167060, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6167090, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "origin", lookup_key: Simple { key: "origin", py_key: Py( 0x00007f42c6165c50, ), path: LookupPath( [ S( "origin", Py( 0x00007f42c6164e40, ), ), ], ), }, name_py: Py( 0x00007f42cb03bec0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "x_axis", lookup_key: Simple { key: "x_axis", py_key: Py( 0x00007f42c6164ed0, ), path: LookupPath( [ S( "x_axis", Py( 0x00007f42c6165aa0, ), ), ], ), }, name_py: Py( 0x00007f42c680a910, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "y_axis", lookup_key: Simple { key: "y_axis", py_key: Py( 0x00007f42c6165470, ), path: LookupPath( [ S( "y_axis", Py( 0x00007f42c61651d0, ), ), ], ), }, name_py: Py( 0x00007f42c680aa00, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "z_axis", lookup_key: Simple { key: "z_axis", py_key: Py( 0x00007f42c6167000, ), path: LookupPath( [ S( "z_axis", Py( 0x00007f42c6167030, ), ), ], ), }, name_py: Py( 0x00007f42c680aaf0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "GetSketchModePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f7dda0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "GetSketchModePlane", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61670c0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61670f0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e3830, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "get_sketch_mode_plane": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e3830, ), ], }, expected_repr: "'get_sketch_mode_plane'", name: "literal['get_sketch_mode_plane']", }, ), validate_default: false, copy_default: false, name: "default[literal['get_sketch_mode_plane']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionGetSketchModePlane", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93596c30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionGetSketchModePlane", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.get_sketch_mode_plane.GetSketchModePlane, type: Literal['get_sketch_mode_plane'] = 'get_sketch_mode_plane') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: GetSketchModePlane[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=GetSketchModePlane, required=True), 'type': FieldInfo(annotation=Literal['get_sketch_mode_plane'], required=False, default='get_sketch_mode_plane')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['get_sketch_mode_plane'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity(**data)[source][source]

The response to the ‘HighlightSetEntity’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['highlight_set_entity']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>, 'config': {'title': 'OptionHighlightSetEntity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionHighlightSetEntity:94483141349088', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, 'config': {'title': 'HighlightSetEntity'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.highlight_set_entity.HighlightSetEntity'>>]}, 'ref': 'kittycad.models.highlight_set_entity.HighlightSetEntity:94483156300848', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'sequence': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'int'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'HighlightSetEntity', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'highlight_set_entity', 'schema': {'expected': ['highlight_set_entity'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionHighlightSetEntity', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9213cee0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e31f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "highlight_set_entity", }, expected_py: None, name: "literal['highlight_set_entity']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f7f430, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007f42c67ea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "sequence": SerField { key_py: Py( 0x00007f42cb03cc48, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Int( IntSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "HighlightSetEntity", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionHighlightSetEntity", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionHighlightSetEntity", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6325200, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6325230, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007f42c63d91b0, ), path: LookupPath( [ S( "entity_id", Py( 0x00007f42c63d9430, ), ), ], ), }, name_py: Py( 0x00007f42c67ea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "sequence", lookup_key: Simple { key: "sequence", py_key: Py( 0x00007f42c63d88f0, ), path: LookupPath( [ S( "sequence", Py( 0x00007f42c63d9070, ), ), ], ), }, name_py: Py( 0x00007f42cb03cc48, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Int( IntValidator { strict: false, }, ), name: "nullable[int]", }, ), validate_default: false, copy_default: false, name: "default[nullable[int]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "HighlightSetEntity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f7f430, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "HighlightSetEntity", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6325260, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6325290, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e31f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "highlight_set_entity": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e31f0, ), ], }, expected_repr: "'highlight_set_entity'", name: "literal['highlight_set_entity']", }, ), validate_default: false, copy_default: false, name: "default[literal['highlight_set_entity']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionHighlightSetEntity", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9213cee0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionHighlightSetEntity", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.highlight_set_entity.HighlightSetEntity, type: Literal['highlight_set_entity'] = 'highlight_set_entity') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: HighlightSetEntity[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=HighlightSetEntity, required=True), 'type': FieldInfo(annotation=Literal['highlight_set_entity'], required=False, default='highlight_set_entity')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['highlight_set_entity'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionImportFiles(**data)[source][source]

The response to the ‘ImportFiles’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.import_files.ImportFiles'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['import_files']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>, 'config': {'title': 'OptionImportFiles'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionImportFiles:94483162592704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.import_files.ImportFiles'>, 'config': {'title': 'ImportFiles'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.import_files.ImportFiles'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.import_files.ImportFiles'>>]}, 'ref': 'kittycad.models.import_files.ImportFiles:94483156373248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'object_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'ImportFiles', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'import_files', 'schema': {'expected': ['import_files'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionImportFiles', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9357f5c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e2eb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "import_files", }, expected_py: None, name: "literal['import_files']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f90f00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "object_id": SerField { key_py: Py( 0x00007f42c94a69f0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ImportFiles", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionImportFiles", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionImportFiles", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61654a0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6165500, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "object_id", lookup_key: Simple { key: "object_id", py_key: Py( 0x00007f42c6163e30, ), path: LookupPath( [ S( "object_id", Py( 0x00007f42c6163d70, ), ), ], ), }, name_py: Py( 0x00007f42c94a69f0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "ImportFiles", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f90f00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ImportFiles", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6165110, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61651a0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e2eb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "import_files": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e2eb0, ), ], }, expected_repr: "'import_files'", name: "literal['import_files']", }, ), validate_default: false, copy_default: false, name: "default[literal['import_files']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionImportFiles", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9357f5c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionImportFiles", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.import_files.ImportFiles, type: Literal['import_files'] = 'import_files') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ImportFiles[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ImportFiles, required=True), 'type': FieldInfo(annotation=Literal['import_files'], required=False, default='import_files')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['import_files'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry(**data)[source][source]

The response to the ‘ImportedGeometry’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.imported_geometry.ImportedGeometry'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['imported_geometry']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>, 'config': {'title': 'OptionImportedGeometry'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionImportedGeometry:94483162611248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.imported_geometry.ImportedGeometry'>, 'config': {'title': 'ImportedGeometry'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.imported_geometry.ImportedGeometry'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.imported_geometry.ImportedGeometry'>>]}, 'ref': 'kittycad.models.imported_geometry.ImportedGeometry:94483156382064', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}, 'value': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'ImportedGeometry', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'imported_geometry', 'schema': {'expected': ['imported_geometry'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionImportedGeometry', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93583e30, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e2d70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "imported_geometry", }, expected_py: None, name: "literal['imported_geometry']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92f93170, ), serializer: Fields( GeneralFieldsSerializer { fields: { "value": SerField { key_py: Py( 0x00007f42cb03dc20, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "id": SerField { key_py: Py( 0x00007f42cb03a4e8, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "ImportedGeometry", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionImportedGeometry", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionImportedGeometry", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6165e30, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6165e60, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "id", lookup_key: Simple { key: "id", py_key: Py( 0x00007f42c6165d70, ), path: LookupPath( [ S( "id", Py( 0x00007f42c6165da0, ), ), ], ), }, name_py: Py( 0x00007f42cb03a4e8, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, Field { name: "value", lookup_key: Simple { key: "value", py_key: Py( 0x00007f42c6165dd0, ), path: LookupPath( [ S( "value", Py( 0x00007f42c6165e00, ), ), ], ), }, name_py: Py( 0x00007f42cb03dc20, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "ImportedGeometry", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f93170, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ImportedGeometry", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6165e90, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6165ec0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e2d70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "imported_geometry": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e2d70, ), ], }, expected_repr: "'imported_geometry'", name: "literal['imported_geometry']", }, ), validate_default: false, copy_default: false, name: "default[literal['imported_geometry']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionImportedGeometry", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93583e30, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionImportedGeometry", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.imported_geometry.ImportedGeometry, type: Literal['imported_geometry'] = 'imported_geometry') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ImportedGeometry[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ImportedGeometry, required=True), 'type': FieldInfo(annotation=Literal['imported_geometry'], required=False, default='imported_geometry')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['imported_geometry'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionLoft(**data)[source][source]

The response to the ‘Loft’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.loft.Loft'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['loft']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>, 'config': {'title': 'OptionLoft'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionLoft'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionLoft:94483161974960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.loft.Loft'>, 'config': {'title': 'Loft'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.loft.Loft'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.loft.Loft'>>]}, 'ref': 'kittycad.models.loft.Loft:94483156647792', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'solid_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'Loft', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'loft', 'schema': {'expected': ['loft'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionLoft', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934e88b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92fd3f70, ), serializer: Fields( GeneralFieldsSerializer { fields: { "solid_id": SerField { key_py: Py( 0x00007f42c670a970, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Loft", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c77ca2e0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "loft", }, expected_py: None, name: "literal['loft']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionLoft", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionLoft", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6327390, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63273c0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "solid_id", lookup_key: Simple { key: "solid_id", py_key: Py( 0x00007f42c640dd70, ), path: LookupPath( [ S( "solid_id", Py( 0x00007f42c640cf70, ), ), ], ), }, name_py: Py( 0x00007f42c670a970, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "Loft", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92fd3f70, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Loft", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63273f0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6327420, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c77ca2e0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "loft": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c77ca2e0, ), ], }, expected_repr: "'loft'", name: "literal['loft']", }, ), validate_default: false, copy_default: false, name: "default[literal['loft']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionLoft", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934e88b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionLoft", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.loft.Loft, type: Literal['loft'] = 'loft') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Loft[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Loft, required=True), 'type': FieldInfo(annotation=Literal['loft'], required=False, default='loft')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['loft'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionMass(**data)[source][source]

The response to the ‘Mass’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.mass.Mass'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mass']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>, 'config': {'title': 'OptionMass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMass'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMass:94483162622432', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.mass.Mass'>, 'config': {'title': 'Mass'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.mass.Mass'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.mass.Mass'>>]}, 'ref': 'kittycad.models.mass.Mass:94483156653264', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'mass': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitMass'>, 'members': [UnitMass.G, UnitMass.KG, UnitMass.LB], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_mass.UnitMass:94483150324624', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}}, 'model_name': 'Mass', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mass', 'schema': {'expected': ['mass'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMass', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935869e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92fd54d0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "output_unit": SerField { key_py: Py( 0x00007f42c71c2f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee929cc390, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "mass": SerField { key_py: Py( 0x00007f42c77ca400, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Mass", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c77ca400, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mass", }, expected_py: None, name: "literal['mass']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMass", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMass", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61663d0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6166400, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "mass", lookup_key: Simple { key: "mass", py_key: Py( 0x00007f42c6166370, ), path: LookupPath( [ S( "mass", Py( 0x00007f42c61663a0, ), ), ], ), }, name_py: Py( 0x00007f42c77ca400, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f42c6184170, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f42c6184130, ), ), ], ), }, name_py: Py( 0x00007f42c71c2f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee929cc390, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "lb": 2, "g": 0, "kg": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c69cd8b0, ), Py( 0x00007f42c69cd910, ), Py( 0x00007f42c69cd970, ), ], }, missing: None, expected_repr: "'g', 'kg' or 'lb'", strict: false, class_repr: "UnitMass", name: "str-enum[UnitMass]", }, ), frozen: false, }, ], model_name: "Mass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92fd54d0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Mass", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6166430, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6166460, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c77ca400, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mass": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c77ca400, ), ], }, expected_repr: "'mass'", name: "literal['mass']", }, ), validate_default: false, copy_default: false, name: "default[literal['mass']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionMass", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935869e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionMass", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.mass.Mass, type: Literal['mass'] = 'mass') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Mass[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Mass, required=True), 'type': FieldInfo(annotation=Literal['mass'], required=False, default='mass')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['mass'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionMouseClick(**data)[source][source]

The response to the ‘MouseClick’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.mouse_click.MouseClick'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['mouse_click']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>, 'config': {'title': 'OptionMouseClick'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionMouseClick:94483162368160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.mouse_click.MouseClick'>, 'config': {'title': 'MouseClick'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.mouse_click.MouseClick'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.mouse_click.MouseClick'>>]}, 'ref': 'kittycad.models.mouse_click.MouseClick:94483160465760', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entities_modified': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}, 'entities_selected': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'MouseClick', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'mouse_click', 'schema': {'expected': ['mouse_click'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionMouseClick', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935488a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee93378160, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entities_modified": SerField { key_py: Py( 0x00007f42c63c72f0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, "entities_selected": SerField { key_py: Py( 0x00007f42c636f4b0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "MouseClick", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c74e18b0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "mouse_click", }, expected_py: None, name: "literal['mouse_click']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionMouseClick", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionMouseClick", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63df1b0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63df1e0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entities_modified", lookup_key: Simple { key: "entities_modified", py_key: Py( 0x00007f42c61531b0, ), path: LookupPath( [ S( "entities_modified", Py( 0x00007f42c6153170, ), ), ], ), }, name_py: Py( 0x00007f42c63c72f0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, Field { name: "entities_selected", lookup_key: Simple { key: "entities_selected", py_key: Py( 0x00007f42c6153230, ), path: LookupPath( [ S( "entities_selected", Py( 0x00007f42c61531f0, ), ), ], ), }, name_py: Py( 0x00007f42c636f4b0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "MouseClick", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93378160, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "MouseClick", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63df210, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63df240, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c74e18b0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "mouse_click": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c74e18b0, ), ], }, expected_repr: "'mouse_click'", name: "literal['mouse_click']", }, ), validate_default: false, copy_default: false, name: "default[literal['mouse_click']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionMouseClick", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935488a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionMouseClick", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.mouse_click.MouseClick, type: Literal['mouse_click'] = 'mouse_click') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: MouseClick[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=MouseClick, required=True), 'type': FieldInfo(annotation=Literal['mouse_click'], required=False, default='mouse_click')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['mouse_click'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid(**data)[source][source]

The response to the ‘PathGetCurveUuid’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_curve_uuid']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>, 'config': {'title': 'OptionPathGetCurveUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuid:94483162445152', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, 'config': {'title': 'PathGetCurveUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid'>>]}, 'ref': 'kittycad.models.path_get_curve_uuid.PathGetCurveUuid:94483159132352', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'PathGetCurveUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuid', 'schema': {'expected': ['path_get_curve_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuid', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9355b560, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73a2ef0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuid", }, expected_py: None, name: "literal['path_get_curve_uuid']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee932328c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_id": SerField { key_py: Py( 0x00007f42c6644eb0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetCurveUuid", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuid", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dfd80, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dfdb0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007f42c615bd70, ), path: LookupPath( [ S( "curve_id", Py( 0x00007f42c615bd30, ), ), ], ), }, name_py: Py( 0x00007f42c6644eb0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "PathGetCurveUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee932328c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathGetCurveUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dfde0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dfe10, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73a2ef0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73a2ef0, ), ], }, expected_repr: "'path_get_curve_uuid'", name: "literal['path_get_curve_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuid']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9355b560, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathGetCurveUuid", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_curve_uuid.PathGetCurveUuid, type: Literal['path_get_curve_uuid'] = 'path_get_curve_uuid') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathGetCurveUuid[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetCurveUuid, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuid'], required=False, default='path_get_curve_uuid')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_get_curve_uuid'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices(**data)[source][source]

The response to the ‘PathGetCurveUuidsForVertices’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_curve_uuids_for_vertices']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>, 'config': {'title': 'OptionPathGetCurveUuidsForVertices'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetCurveUuidsForVertices:94483162429088', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, 'config': {'title': 'PathGetCurveUuidsForVertices'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices'>>]}, 'ref': 'kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices:94483161621488', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'curve_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetCurveUuidsForVertices', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_curve_uuids_for_vertices', 'schema': {'expected': ['path_get_curve_uuids_for_vertices'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetCurveUuidsForVertices', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935576a0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738a790, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_curve_uuids_for_vertices", }, expected_py: None, name: "literal['path_get_curve_uuids_for_vertices']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934923f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_ids": SerField { key_py: Py( 0x00007f42c6322670, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetCurveUuidsForVertices", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetCurveUuidsForVertices", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63de070, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63de370, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "curve_ids", lookup_key: Simple { key: "curve_ids", py_key: Py( 0x00007f42c66232b0, ), path: LookupPath( [ S( "curve_ids", Py( 0x00007f42c615aaf0, ), ), ], ), }, name_py: Py( 0x00007f42c6322670, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetCurveUuidsForVertices", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934923f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathGetCurveUuidsForVertices", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de340, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63de6a0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738a790, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_curve_uuids_for_vertices": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738a790, ), ], }, expected_repr: "'path_get_curve_uuids_for_vertices'", name: "literal['path_get_curve_uuids_for_vertices']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_curve_uuids_for_vertices']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathGetCurveUuidsForVertices", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935576a0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathGetCurveUuidsForVertices", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_curve_uuids_for_vertices.PathGetCurveUuidsForVertices, type: Literal['path_get_curve_uuids_for_vertices'] = 'path_get_curve_uuids_for_vertices') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathGetCurveUuidsForVertices[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetCurveUuidsForVertices, required=True), 'type': FieldInfo(annotation=Literal['path_get_curve_uuids_for_vertices'], required=False, default='path_get_curve_uuids_for_vertices')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_get_curve_uuids_for_vertices'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo(**data)[source][source]

The response to the ‘PathGetInfo’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_info.PathGetInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_info']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>, 'config': {'title': 'OptionPathGetInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetInfo:94483162393952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_info.PathGetInfo'>, 'config': {'title': 'PathGetInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_info.PathGetInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_info.PathGetInfo'>>]}, 'ref': 'kittycad.models.path_get_info.PathGetInfo:94483161851184', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'segments': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'config': {'title': 'PathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment_info.PathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment_info.PathSegmentInfo'>>]}, 'ref': 'kittycad.models.path_segment_info.PathSegmentInfo:94483161872048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'command': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathCommand'>, 'members': [PathCommand.MOVE_TO, PathCommand.LINE_TO, PathCommand.BEZ_CURVE_TO, PathCommand.NURBS_CURVE_TO, PathCommand.ADD_ARC], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_command.PathCommand:94483161869168', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'command_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}}, 'model_name': 'PathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_info', 'schema': {'expected': ['path_get_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetInfo', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9354ed60, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934ca530, ), serializer: Fields( GeneralFieldsSerializer { fields: { "segments": SerField { key_py: Py( 0x00007f42c9e4e430, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055ee934cf6b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "relative": SerField { key_py: Py( 0x00007f42c97ccf70, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "command": SerField { key_py: Py( 0x00007f42cb038de0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee934ceb70, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "command_id": SerField { key_py: Py( 0x00007f42c6323b30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "PathSegmentInfo", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[PathSegmentInfo]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73a2ff0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_info", }, expected_py: None, name: "literal['path_get_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetInfo", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63a7fc0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63a7480, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "segments", lookup_key: Simple { key: "segments", py_key: Py( 0x00007f42c614f0f0, ), path: LookupPath( [ S( "segments", Py( 0x00007f42c6152830, ), ), ], ), }, name_py: Py( 0x00007f42c9e4e430, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "command", lookup_key: Simple { key: "command", py_key: Py( 0x00007f42c63a7f00, ), path: LookupPath( [ S( "command", Py( 0x00007f42c63a6160, ), ), ], ), }, name_py: Py( 0x00007f42cb038de0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee934ceb70, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "line_to": 1, "add_arc": 4, "bez_curve_to": 2, "move_to": 0, "nurbs_curve_to": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c65ce8d0, ), Py( 0x00007f42c65ce990, ), Py( 0x00007f42c65ce9f0, ), Py( 0x00007f42c65cea50, ), Py( 0x00007f42c65ceb10, ), ], }, missing: None, expected_repr: "'move_to', 'line_to', 'bez_curve_to', 'nurbs_curve_to' or 'add_arc'", strict: false, class_repr: "PathCommand", name: "str-enum[PathCommand]", }, ), frozen: false, }, Field { name: "command_id", lookup_key: Simple { key: "command_id", py_key: Py( 0x00007f42c63d3db0, ), path: LookupPath( [ S( "command_id", Py( 0x00007f42c637d630, ), ), ], ), }, name_py: Py( 0x00007f42c6323b30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055ee930485e0, ), config: Py( 0x00007f42c615a280, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), name: "nullable[function-after[ModelingCmdId(), str]]", }, ), validate_default: false, copy_default: false, name: "default[nullable[function-after[ModelingCmdId(), str]]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007f42c6406970, ), path: LookupPath( [ S( "relative", Py( 0x00007f42c6404c30, ), ), ], ), }, name_py: Py( 0x00007f42c97ccf70, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, ], model_name: "PathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934cf6b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathSegmentInfo", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934ca530, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathGetInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63a5bf0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63a4c00, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73a2ff0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73a2ff0, ), ], }, expected_repr: "'path_get_info'", name: "literal['path_get_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_info']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathGetInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9354ed60, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathGetInfo", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_info.PathGetInfo, type: Literal['path_get_info'] = 'path_get_info') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathGetInfo[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetInfo, required=True), 'type': FieldInfo(annotation=Literal['path_get_info'], required=False, default='path_get_info')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_get_info'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid(**data)[source][source]

The response to the ‘PathGetSketchTargetUuid’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_sketch_target_uuid']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>, 'config': {'title': 'OptionPathGetSketchTargetUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetSketchTargetUuid:94483162468704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, 'config': {'title': 'PathGetSketchTargetUuid'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid'>>]}, 'ref': 'kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid:94483161630080', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'target_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PathGetSketchTargetUuid', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_sketch_target_uuid', 'schema': {'expected': ['path_get_sketch_target_uuid'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetSketchTargetUuid', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93561160, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738a830, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_sketch_target_uuid", }, expected_py: None, name: "literal['path_get_sketch_target_uuid']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee93494580, ), serializer: Fields( GeneralFieldsSerializer { fields: { "target_id": SerField { key_py: Py( 0x00007f42cb02a560, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetSketchTargetUuid", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetSketchTargetUuid", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetSketchTargetUuid", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61647e0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6164810, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "target_id", lookup_key: Simple { key: "target_id", py_key: Py( 0x00007f42c61626f0, ), path: LookupPath( [ S( "target_id", Py( 0x00007f42c61626b0, ), ), ], ), }, name_py: Py( 0x00007f42cb02a560, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "PathGetSketchTargetUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93494580, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathGetSketchTargetUuid", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6164840, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6164870, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738a830, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_sketch_target_uuid": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738a830, ), ], }, expected_repr: "'path_get_sketch_target_uuid'", name: "literal['path_get_sketch_target_uuid']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_sketch_target_uuid']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathGetSketchTargetUuid", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93561160, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathGetSketchTargetUuid", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_sketch_target_uuid.PathGetSketchTargetUuid, type: Literal['path_get_sketch_target_uuid'] = 'path_get_sketch_target_uuid') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathGetSketchTargetUuid[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetSketchTargetUuid, required=True), 'type': FieldInfo(annotation=Literal['path_get_sketch_target_uuid'], required=False, default='path_get_sketch_target_uuid')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_get_sketch_target_uuid'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids(**data)[source][source]

The response to the ‘PathGetVertexUuids’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_get_vertex_uuids']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>, 'config': {'title': 'OptionPathGetVertexUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathGetVertexUuids:94483162457440', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, 'config': {'title': 'PathGetVertexUuids'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids'>>]}, 'ref': 'kittycad.models.path_get_vertex_uuids.PathGetVertexUuids:94483161637312', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'vertex_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'PathGetVertexUuids', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_get_vertex_uuids', 'schema': {'expected': ['path_get_vertex_uuids'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathGetVertexUuids', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9355e560, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73a3170, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_get_vertex_uuids", }, expected_py: None, name: "literal['path_get_vertex_uuids']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934961c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "vertex_ids": SerField { key_py: Py( 0x00007f42c65bc330, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PathGetVertexUuids", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathGetVertexUuids", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathGetVertexUuids", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c61642d0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6164300, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "vertex_ids", lookup_key: Simple { key: "vertex_ids", py_key: Py( 0x00007f42c6161230, ), path: LookupPath( [ S( "vertex_ids", Py( 0x00007f42c61611f0, ), ), ], ), }, name_py: Py( 0x00007f42c65bc330, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "PathGetVertexUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934961c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathGetVertexUuids", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6164330, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6164360, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73a3170, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_get_vertex_uuids": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73a3170, ), ], }, expected_repr: "'path_get_vertex_uuids'", name: "literal['path_get_vertex_uuids']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_get_vertex_uuids']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathGetVertexUuids", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9355e560, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathGetVertexUuids", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_get_vertex_uuids.PathGetVertexUuids, type: Literal['path_get_vertex_uuids'] = 'path_get_vertex_uuids') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathGetVertexUuids[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathGetVertexUuids, required=True), 'type': FieldInfo(annotation=Literal['path_get_vertex_uuids'], required=False, default='path_get_vertex_uuids')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_get_vertex_uuids'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo(**data)[source][source]

The response to the ‘PathSegmentInfo’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['path_segment_info']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>, 'config': {'title': 'OptionPathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPathSegmentInfo:94483162408736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.path_segment_info.PathSegmentInfo'>, 'config': {'title': 'PathSegmentInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.path_segment_info.PathSegmentInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.path_segment_info.PathSegmentInfo'>>]}, 'ref': 'kittycad.models.path_segment_info.PathSegmentInfo:94483161872048', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'command': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'PathCommand'>, 'members': [PathCommand.MOVE_TO, PathCommand.LINE_TO, PathCommand.BEZ_CURVE_TO, PathCommand.NURBS_CURVE_TO, PathCommand.ADD_ARC], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.path_command.PathCommand:94483161869168', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'command_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'function': {'function': <class 'kittycad.models.modeling_cmd_id.ModelingCmdId'>, 'type': 'no-info'}, 'schema': {'type': 'str'}, 'type': 'function-after'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'relative': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}}, 'model_name': 'PathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'path_segment_info', 'schema': {'expected': ['path_segment_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPathSegmentInfo', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93552720, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934cf6b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "command": SerField { key_py: Py( 0x00007f42cb038de0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee934ceb70, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, "command_id": SerField { key_py: Py( 0x00007f42c6323b30, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "relative": SerField { key_py: Py( 0x00007f42c97ccf70, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "PathSegmentInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73a32f0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "path_segment_info", }, expected_py: None, name: "literal['path_segment_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPathSegmentInfo", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPathSegmentInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63df120, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63defd0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "command", lookup_key: Simple { key: "command", py_key: Py( 0x00007f42c63df420, ), path: LookupPath( [ S( "command", Py( 0x00007f42c63df090, ), ), ], ), }, name_py: Py( 0x00007f42cb038de0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee934ceb70, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "nurbs_curve_to": 3, "move_to": 0, "line_to": 1, "add_arc": 4, "bez_curve_to": 2, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c65ce8d0, ), Py( 0x00007f42c65ce990, ), Py( 0x00007f42c65ce9f0, ), Py( 0x00007f42c65cea50, ), Py( 0x00007f42c65ceb10, ), ], }, missing: None, expected_repr: "'move_to', 'line_to', 'bez_curve_to', 'nurbs_curve_to' or 'add_arc'", strict: false, class_repr: "PathCommand", name: "str-enum[PathCommand]", }, ), frozen: false, }, Field { name: "command_id", lookup_key: Simple { key: "command_id", py_key: Py( 0x00007f42c61593b0, ), path: LookupPath( [ S( "command_id", Py( 0x00007f42c6158ef0, ), ), ], ), }, name_py: Py( 0x00007f42c6323b30, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: FunctionAfter( FunctionAfterValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), func: Py( 0x000055ee930485e0, ), config: Py( 0x00007f42c650fdc0, ), name: "function-after[ModelingCmdId(), str]", field_name: None, info_arg: false, }, ), name: "nullable[function-after[ModelingCmdId(), str]]", }, ), validate_default: false, copy_default: false, name: "default[nullable[function-after[ModelingCmdId(), str]]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "relative", lookup_key: Simple { key: "relative", py_key: Py( 0x00007f42c61595b0, ), path: LookupPath( [ S( "relative", Py( 0x00007f42c6158e30, ), ), ], ), }, name_py: Py( 0x00007f42c97ccf70, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, ], model_name: "PathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934cf6b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PathSegmentInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63df000, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dd8c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73a32f0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "path_segment_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73a32f0, ), ], }, expected_repr: "'path_segment_info'", name: "literal['path_segment_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['path_segment_info']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPathSegmentInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93552720, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPathSegmentInfo", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.path_segment_info.PathSegmentInfo, type: Literal['path_segment_info'] = 'path_segment_info') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PathSegmentInfo[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PathSegmentInfo, required=True), 'type': FieldInfo(annotation=Literal['path_segment_info'], required=False, default='path_segment_info')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['path_segment_info'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject(**data)[source][source]

The response to the ‘PlaneIntersectAndProject’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['plane_intersect_and_project']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>, 'config': {'title': 'OptionPlaneIntersectAndProject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionPlaneIntersectAndProject:94483162572784', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, 'config': {'title': 'PlaneIntersectAndProject'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject'>>]}, 'ref': 'kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject:94483161690400', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'plane_coordinates': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'cls': <class 'kittycad.models.point2d.Point2d'>, 'config': {'title': 'Point2d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point2d.Point2d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point2d.Point2d'>>]}, 'ref': 'kittycad.models.point2d.Point2d:94483157020960', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <class 'kittycad.models.length_unit.LengthUnit'>, 'type': 'no-info'}, 'schema': {'type': 'float'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'Point2d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'PlaneIntersectAndProject', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'plane_intersect_and_project', 'schema': {'expected': ['plane_intersect_and_project'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionPlaneIntersectAndProject', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9357a7f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a3120, ), serializer: Fields( GeneralFieldsSerializer { fields: { "plane_coordinates": SerField { key_py: Py( 0x00007f42c63abeb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Model( ModelSerializer { class: Py( 0x000055ee9302f120, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Point2d", }, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "PlaneIntersectAndProject", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738a970, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "plane_intersect_and_project", }, expected_py: None, name: "literal['plane_intersect_and_project']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionPlaneIntersectAndProject", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionPlaneIntersectAndProject", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6165a70, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6165b30, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "plane_coordinates", lookup_key: Simple { key: "plane_coordinates", py_key: Py( 0x00007f42c6173770, ), path: LookupPath( [ S( "plane_coordinates", Py( 0x00007f42c61727f0, ), ), ], ), }, name_py: Py( 0x00007f42c63abeb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055ee92ecce10, ), config: Py( 0x00007f42c617c940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: FunctionAfter( FunctionAfterValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), func: Py( 0x000055ee92ecce10, ), config: Py( 0x00007f42c617c940, ), name: "function-after[LengthUnit(), float]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "Point2d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9302f120, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point2d", }, ), name: "nullable[Point2d]", }, ), validate_default: false, copy_default: false, name: "default[nullable[Point2d]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "PlaneIntersectAndProject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a3120, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "PlaneIntersectAndProject", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6165b60, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6165b00, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738a970, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "plane_intersect_and_project": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738a970, ), ], }, expected_repr: "'plane_intersect_and_project'", name: "literal['plane_intersect_and_project']", }, ), validate_default: false, copy_default: false, name: "default[literal['plane_intersect_and_project']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionPlaneIntersectAndProject", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9357a7f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionPlaneIntersectAndProject", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.plane_intersect_and_project.PlaneIntersectAndProject, type: Literal['plane_intersect_and_project'] = 'plane_intersect_and_project') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: PlaneIntersectAndProject[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=PlaneIntersectAndProject, required=True), 'type': FieldInfo(annotation=Literal['plane_intersect_and_project'], required=False, default='plane_intersect_and_project')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['plane_intersect_and_project'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSelectGet(**data)[source][source]

The response to the ‘SelectGet’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_get.SelectGet'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_get']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>, 'config': {'title': 'OptionSelectGet'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectGet:94483162253248', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_get.SelectGet'>, 'config': {'title': 'SelectGet'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_get.SelectGet'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_get.SelectGet'>>]}, 'ref': 'kittycad.models.select_get.SelectGet:94483161652704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_ids': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'SelectGet', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_get', 'schema': {'expected': ['select_get'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectGet', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9352c7c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee93499de0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_ids": SerField { key_py: Py( 0x00007f42c67e8870, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "SelectGet", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73a3f70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_get", }, expected_py: None, name: "literal['select_get']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectGet", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectGet", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dd950, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dd6b0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_ids", lookup_key: Simple { key: "entity_ids", py_key: Py( 0x00007f42c637fbb0, ), path: LookupPath( [ S( "entity_ids", Py( 0x00007f42c637ffb0, ), ), ], ), }, name_py: Py( 0x00007f42c67e8870, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "SelectGet", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93499de0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "SelectGet", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dd560, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dd590, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73a3f70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_get": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73a3f70, ), ], }, expected_repr: "'select_get'", name: "literal['select_get']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_get']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSelectGet", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9352c7c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSelectGet", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_get.SelectGet, type: Literal['select_get'] = 'select_get') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: SelectGet[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectGet, required=True), 'type': FieldInfo(annotation=Literal['select_get'], required=False, default='select_get')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['select_get'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint(**data)[source][source]

The response to the ‘SelectWithPoint’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.select_with_point.SelectWithPoint'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['select_with_point']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>, 'config': {'title': 'OptionSelectWithPoint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSelectWithPoint:94483161898768', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.select_with_point.SelectWithPoint'>, 'config': {'title': 'SelectWithPoint'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.select_with_point.SelectWithPoint'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.select_with_point.SelectWithPoint'>>]}, 'ref': 'kittycad.models.select_with_point.SelectWithPoint:94483161678208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'entity_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'SelectWithPoint', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'select_with_point', 'schema': {'expected': ['select_with_point'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSelectWithPoint', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee934d5f10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a0180, ), serializer: Fields( GeneralFieldsSerializer { fields: { "entity_id": SerField { key_py: Py( 0x00007f42c67ea670, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "SelectWithPoint", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73dc070, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "select_with_point", }, expected_py: None, name: "literal['select_with_point']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSelectWithPoint", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSelectWithPoint", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6324cf0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6324d20, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "entity_id", lookup_key: Simple { key: "entity_id", py_key: Py( 0x00007f42c639d470, ), path: LookupPath( [ S( "entity_id", Py( 0x00007f42c639d270, ), ), ], ), }, name_py: Py( 0x00007f42c67ea670, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "SelectWithPoint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a0180, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "SelectWithPoint", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6324d50, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6324d80, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73dc070, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "select_with_point": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73dc070, ), ], }, expected_repr: "'select_with_point'", name: "literal['select_with_point']", }, ), validate_default: false, copy_default: false, name: "default[literal['select_with_point']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSelectWithPoint", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934d5f10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSelectWithPoint", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.select_with_point.SelectWithPoint, type: Literal['select_with_point'] = 'select_with_point') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: SelectWithPoint[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SelectWithPoint, required=True), 'type': FieldInfo(annotation=Literal['select_with_point'], required=False, default='select_with_point')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['select_with_point'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces(**data)[source][source]

The response to the ‘Solid3dGetAllEdgeFaces’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_all_edge_faces']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>, 'config': {'title': 'OptionSolid3DGetAllEdgeFaces'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllEdgeFaces:94483162262512', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, 'config': {'title': 'Solid3dGetAllEdgeFaces'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces'>>]}, 'ref': 'kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces:94483161684400', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetAllEdgeFaces', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_edge_faces', 'schema': {'expected': ['solid3d_get_all_edge_faces'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllEdgeFaces', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9352ebf0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a19b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "faces": SerField { key_py: Py( 0x00007f42c63a5da0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetAllEdgeFaces", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738aba0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_edge_faces", }, expected_py: None, name: "literal['solid3d_get_all_edge_faces']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllEdgeFaces", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dc990, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dc9c0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "faces", lookup_key: Simple { key: "faces", py_key: Py( 0x00007f42c63dca50, ), path: LookupPath( [ S( "faces", Py( 0x00007f42c63dcae0, ), ), ], ), }, name_py: Py( 0x00007f42c63a5da0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetAllEdgeFaces", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a19b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetAllEdgeFaces", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dc930, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dcb10, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738aba0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_edge_faces": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738aba0, ), ], }, expected_repr: "'solid3d_get_all_edge_faces'", name: "literal['solid3d_get_all_edge_faces']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_edge_faces']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllEdgeFaces", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9352ebf0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetAllEdgeFaces", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_all_edge_faces.Solid3dGetAllEdgeFaces, type: Literal['solid3d_get_all_edge_faces'] = 'solid3d_get_all_edge_faces') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetAllEdgeFaces[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetAllEdgeFaces, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_edge_faces'], required=False, default='solid3d_get_all_edge_faces')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_all_edge_faces'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges(**data)[source][source]

The response to the ‘Solid3dGetAllOppositeEdges’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_all_opposite_edges']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>, 'config': {'title': 'OptionSolid3DGetAllOppositeEdges'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetAllOppositeEdges:94483162274496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, 'config': {'title': 'Solid3dGetAllOppositeEdges'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges'>>]}, 'ref': 'kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges:94483161695664', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edges': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'type': 'str'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetAllOppositeEdges', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_all_opposite_edges', 'schema': {'expected': ['solid3d_get_all_opposite_edges'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetAllOppositeEdges', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93531ac0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a45b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edges": SerField { key_py: Py( 0x00007f42cb02a4a0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Str( StrSerializer, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[str]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetAllOppositeEdges", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738abf0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_all_opposite_edges", }, expected_py: None, name: "literal['solid3d_get_all_opposite_edges']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetAllOppositeEdges", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63ddc80, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63ddcb0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edges", lookup_key: Simple { key: "edges", py_key: Py( 0x00007f42c63ddb90, ), path: LookupPath( [ S( "edges", Py( 0x00007f42c63ddc50, ), ), ], ), }, name_py: Py( 0x00007f42cb02a4a0, ), validator: List( ListValidator { strict: false, item_validator: Some( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetAllOppositeEdges", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a45b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetAllOppositeEdges", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63ddce0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63ddd10, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738abf0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_all_opposite_edges": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738abf0, ), ], }, expected_repr: "'solid3d_get_all_opposite_edges'", name: "literal['solid3d_get_all_opposite_edges']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_all_opposite_edges']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetAllOppositeEdges", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93531ac0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetAllOppositeEdges", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_all_opposite_edges.Solid3dGetAllOppositeEdges, type: Literal['solid3d_get_all_opposite_edges'] = 'solid3d_get_all_opposite_edges') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetAllOppositeEdges[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetAllOppositeEdges, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_all_opposite_edges'], required=False, default='solid3d_get_all_opposite_edges')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_all_opposite_edges'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo(**data)[source][source]

The response to the ‘Solid3dGetExtrusionFaceInfo’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_extrusion_face_info']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>, 'config': {'title': 'OptionSolid3DGetExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetExtrusionFaceInfo:94483162775216', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, 'config': {'title': 'Solid3dGetExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo:94483161701392', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'faces': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'items_schema': {'cls': <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, 'config': {'title': 'ExtrusionFaceInfo'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo'>>]}, 'ref': 'kittycad.models.extrusion_face_info.ExtrusionFaceInfo:94483155814256', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'cap': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'ExtrusionFaceCapType'>, 'members': [ExtrusionFaceCapType.NONE, ExtrusionFaceCapType.TOP, ExtrusionFaceCapType.BOTTOM], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.extrusion_face_cap_type.ExtrusionFaceCapType:94483155810096', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'curve_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'face_id': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'ExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'list'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_extrusion_face_info', 'schema': {'expected': ['solid3d_get_extrusion_face_info'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetExtrusionFaceInfo', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee935abeb0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a5c10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "faces": SerField { key_py: Py( 0x00007f42c63a5da0, ), alias: None, alias_py: None, serializer: Some( List( ListSerializer { item_serializer: Model( ModelSerializer { class: Py( 0x000055ee92f08770, ), serializer: Fields( GeneralFieldsSerializer { fields: { "curve_id": SerField { key_py: Py( 0x00007f42c6644eb0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "face_id": SerField { key_py: Py( 0x00007f42c71fe6a0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, "cap": SerField { key_py: Py( 0x00007f42c8eb61f0, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee92f07730, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 3, }, ), has_extra: false, root_model: false, name: "ExtrusionFaceInfo", }, ), filter: SchemaFilter { include: None, exclude: None, }, name: "list[ExtrusionFaceInfo]", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetExtrusionFaceInfo", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738ac90, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_extrusion_face_info", }, expected_py: None, name: "literal['solid3d_get_extrusion_face_info']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetExtrusionFaceInfo", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6166cd0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6166d60, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "faces", lookup_key: Simple { key: "faces", py_key: Py( 0x00007f42c6166c10, ), path: LookupPath( [ S( "faces", Py( 0x00007f42c6166ca0, ), ), ], ), }, name_py: Py( 0x00007f42c63a5da0, ), validator: List( ListValidator { strict: false, item_validator: Some( Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "cap", lookup_key: Simple { key: "cap", py_key: Py( 0x00007f42c6166670, ), path: LookupPath( [ S( "cap", Py( 0x00007f42c6166640, ), ), ], ), }, name_py: Py( 0x00007f42c8eb61f0, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee92f07730, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "bottom": 2, "none": 0, "top": 1, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c67f5e50, ), Py( 0x00007f42c67f5eb0, ), Py( 0x00007f42c67f5f10, ), ], }, missing: None, expected_repr: "'none', 'top' or 'bottom'", strict: false, class_repr: "ExtrusionFaceCapType", name: "str-enum[ExtrusionFaceCapType]", }, ), frozen: false, }, Field { name: "curve_id", lookup_key: Simple { key: "curve_id", py_key: Py( 0x00007f42c619a670, ), path: LookupPath( [ S( "curve_id", Py( 0x00007f42c619a630, ), ), ], ), }, name_py: Py( 0x00007f42c6644eb0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "face_id", lookup_key: Simple { key: "face_id", py_key: Py( 0x00007f42c6166520, ), path: LookupPath( [ S( "face_id", Py( 0x00007f42c6166a90, ), ), ], ), }, name_py: Py( 0x00007f42c71fe6a0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "ExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92f08770, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ExtrusionFaceInfo", }, ), ), min_length: None, max_length: None, name: OnceLock( <uninit>, ), fail_fast: false, }, ), frozen: false, }, ], model_name: "Solid3dGetExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a5c10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetExtrusionFaceInfo", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6166be0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c6164ff0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738ac90, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_extrusion_face_info": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738ac90, ), ], }, expected_repr: "'solid3d_get_extrusion_face_info'", name: "literal['solid3d_get_extrusion_face_info']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_extrusion_face_info']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetExtrusionFaceInfo", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee935abeb0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetExtrusionFaceInfo", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_extrusion_face_info.Solid3dGetExtrusionFaceInfo, type: Literal['solid3d_get_extrusion_face_info'] = 'solid3d_get_extrusion_face_info') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetExtrusionFaceInfo[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetExtrusionFaceInfo, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_extrusion_face_info'], required=False, default='solid3d_get_extrusion_face_info')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_extrusion_face_info'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge(**data)[source][source]

The response to the ‘Solid3dGetNextAdjacentEdge’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_next_adjacent_edge']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetNextAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetNextAdjacentEdge:94483162299296', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, 'config': {'title': 'Solid3dGetNextAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge:94483161667008', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetNextAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_next_adjacent_edge', 'schema': {'expected': ['solid3d_get_next_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetNextAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93537ba0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee9349d5c0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007f42c674bba0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetNextAdjacentEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738ad30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_next_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_next_adjacent_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetNextAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63de760, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63de790, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007f42c63de700, ), path: LookupPath( [ S( "edge", Py( 0x00007f42c63de730, ), ), ], ), }, name_py: Py( 0x00007f42c674bba0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "Solid3dGetNextAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9349d5c0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetNextAdjacentEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de7c0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63de7f0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738ad30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_next_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738ad30, ), ], }, expected_repr: "'solid3d_get_next_adjacent_edge'", name: "literal['solid3d_get_next_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_next_adjacent_edge']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetNextAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93537ba0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetNextAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_next_adjacent_edge.Solid3dGetNextAdjacentEdge, type: Literal['solid3d_get_next_adjacent_edge'] = 'solid3d_get_next_adjacent_edge') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetNextAdjacentEdge[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetNextAdjacentEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_next_adjacent_edge'], required=False, default='solid3d_get_next_adjacent_edge')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_next_adjacent_edge'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge(**data)[source][source]

The response to the ‘Solid3dGetOppositeEdge’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_opposite_edge']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>, 'config': {'title': 'OptionSolid3DGetOppositeEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetOppositeEdge:94483162286864', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, 'config': {'title': 'Solid3dGetOppositeEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge:94483161712736', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'str'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetOppositeEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_opposite_edge', 'schema': {'expected': ['solid3d_get_opposite_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetOppositeEdge', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93534b10, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a8860, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007f42c674bba0, ), alias: None, alias_py: None, serializer: Some( Str( StrSerializer, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetOppositeEdge", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738add0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_opposite_edge", }, expected_py: None, name: "literal['solid3d_get_opposite_edge']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetOppositeEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63de1f0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63de220, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007f42c63de190, ), path: LookupPath( [ S( "edge", Py( 0x00007f42c63de1c0, ), ), ], ), }, name_py: Py( 0x00007f42c674bba0, ), validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), frozen: false, }, ], model_name: "Solid3dGetOppositeEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a8860, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetOppositeEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63de250, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63de280, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738add0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_opposite_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738add0, ), ], }, expected_repr: "'solid3d_get_opposite_edge'", name: "literal['solid3d_get_opposite_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_opposite_edge']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetOppositeEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93534b10, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetOppositeEdge", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_opposite_edge.Solid3dGetOppositeEdge, type: Literal['solid3d_get_opposite_edge'] = 'solid3d_get_opposite_edge') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetOppositeEdge[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetOppositeEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_opposite_edge'], required=False, default='solid3d_get_opposite_edge')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_opposite_edge'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge(**data)[source][source]

The response to the ‘Solid3dGetPrevAdjacentEdge’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['solid3d_get_prev_adjacent_edge']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>, 'config': {'title': 'OptionSolid3DGetPrevAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSolid3DGetPrevAdjacentEdge:94483162310896', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, 'config': {'title': 'Solid3dGetPrevAdjacentEdge'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge'>>]}, 'ref': 'kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge:94483161716704', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'edge': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'str'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'Solid3dGetPrevAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'solid3d_get_prev_adjacent_edge', 'schema': {'expected': ['solid3d_get_prev_adjacent_edge'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSolid3DGetPrevAdjacentEdge', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9353a8f0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c738ae20, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "solid3d_get_prev_adjacent_edge", }, expected_py: None, name: "literal['solid3d_get_prev_adjacent_edge']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934a97e0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "edge": SerField { key_py: Py( 0x00007f42c674bba0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Str( StrSerializer, ), }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "Solid3dGetPrevAdjacentEdge", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSolid3DGetPrevAdjacentEdge", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63decd0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63ded00, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "edge", lookup_key: Simple { key: "edge", py_key: Py( 0x00007f42c63dec70, ), path: LookupPath( [ S( "edge", Py( 0x00007f42c63deca0, ), ), ], ), }, name_py: Py( 0x00007f42c674bba0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), name: "nullable[str]", }, ), validate_default: false, copy_default: false, name: "default[nullable[str]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "Solid3dGetPrevAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934a97e0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Solid3dGetPrevAdjacentEdge", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63ded30, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63ded60, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c738ae20, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "solid3d_get_prev_adjacent_edge": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c738ae20, ), ], }, expected_repr: "'solid3d_get_prev_adjacent_edge'", name: "literal['solid3d_get_prev_adjacent_edge']", }, ), validate_default: false, copy_default: false, name: "default[literal['solid3d_get_prev_adjacent_edge']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSolid3DGetPrevAdjacentEdge", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9353a8f0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSolid3DGetPrevAdjacentEdge", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.solid3d_get_prev_adjacent_edge.Solid3dGetPrevAdjacentEdge, type: Literal['solid3d_get_prev_adjacent_edge'] = 'solid3d_get_prev_adjacent_edge') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Solid3dGetPrevAdjacentEdge[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Solid3dGetPrevAdjacentEdge, required=True), 'type': FieldInfo(annotation=Literal['solid3d_get_prev_adjacent_edge'], required=False, default='solid3d_get_prev_adjacent_edge')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['solid3d_get_prev_adjacent_edge'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea(**data)[source][source]

The response to the ‘SurfaceArea’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.surface_area.SurfaceArea'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['surface_area']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>, 'config': {'title': 'OptionSurfaceArea'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionSurfaceArea:94483162662208', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.surface_area.SurfaceArea'>, 'config': {'title': 'SurfaceArea'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.surface_area.SurfaceArea'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.surface_area.SurfaceArea'>>]}, 'ref': 'kittycad.models.surface_area.SurfaceArea:94483161726720', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitArea'>, 'members': [UnitArea.CM2, UnitArea.DM2, UnitArea.FT2, UnitArea.IN2, UnitArea.KM2, UnitArea.M2, UnitArea.MM2, UnitArea.YD2], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_area.UnitArea:94483150321248', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'surface_area': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'SurfaceArea', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'surface_area', 'schema': {'expected': ['surface_area'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionSurfaceArea', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93590540, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73dcef0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "surface_area", }, expected_py: None, name: "literal['surface_area']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934abf00, ), serializer: Fields( GeneralFieldsSerializer { fields: { "surface_area": SerField { key_py: Py( 0x00007f42c73dcef0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007f42c71c2f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee929cb660, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "SurfaceArea", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionSurfaceArea", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionSurfaceArea", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6166b50, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6166bb0, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f42c617f730, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f42c617f570, ), ), ], ), }, name_py: Py( 0x00007f42c71c2f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee929cb660, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "m2": 5, "km2": 4, "mm2": 6, "dm2": 1, "ft2": 2, "cm2": 0, "yd2": 7, "in2": 3, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c69cd430, ), Py( 0x00007f42c69cd490, ), Py( 0x00007f42c69cd4f0, ), Py( 0x00007f42c69cd550, ), Py( 0x00007f42c69cd5b0, ), Py( 0x00007f42c69cd610, ), Py( 0x00007f42c69cd670, ), Py( 0x00007f42c69cd6d0, ), ], }, missing: None, expected_repr: "'cm2', 'dm2', 'ft2', 'in2', 'km2', 'm2', 'mm2' or 'yd2'", strict: false, class_repr: "UnitArea", name: "str-enum[UnitArea]", }, ), frozen: false, }, Field { name: "surface_area", lookup_key: Simple { key: "surface_area", py_key: Py( 0x00007f42c617f970, ), path: LookupPath( [ S( "surface_area", Py( 0x00007f42c617fcb0, ), ), ], ), }, name_py: Py( 0x00007f42c73dcef0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "SurfaceArea", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934abf00, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "SurfaceArea", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c6166730, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61667f0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73dcef0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "surface_area": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73dcef0, ), ], }, expected_repr: "'surface_area'", name: "literal['surface_area']", }, ), validate_default: false, copy_default: false, name: "default[literal['surface_area']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionSurfaceArea", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93590540, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionSurfaceArea", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.surface_area.SurfaceArea, type: Literal['surface_area'] = 'surface_area') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: SurfaceArea[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=SurfaceArea, required=True), 'type': FieldInfo(annotation=Literal['surface_area'], required=False, default='surface_area')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['surface_area'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot(**data)[source][source]

The response to the ‘TakeSnapshot’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.take_snapshot.TakeSnapshot'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['take_snapshot']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>, 'config': {'title': 'OptionTakeSnapshot'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionTakeSnapshot:94483162379952', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.take_snapshot.TakeSnapshot'>, 'config': {'title': 'TakeSnapshot'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.take_snapshot.TakeSnapshot'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.take_snapshot.TakeSnapshot'>>]}, 'ref': 'kittycad.models.take_snapshot.TakeSnapshot:94483161744240', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'contents': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'function': {'function': <bound method Base64Data.validate of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'no-info'}, 'schema': {'choices': [{'type': 'str'}, {'type': 'bytes'}], 'type': 'union'}, 'serialization': {'function': <bound method Base64Data.serialize of <class 'kittycad.models.base64data.Base64Data'>>, 'type': 'function-plain'}, 'type': 'function-after'}, 'type': 'model-field'}}, 'model_name': 'TakeSnapshot', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'take_snapshot', 'schema': {'expected': ['take_snapshot'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionTakeSnapshot', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9354b6b0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73dcf70, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "take_snapshot", }, expected_py: None, name: "literal['take_snapshot']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934b0370, ), serializer: Fields( GeneralFieldsSerializer { fields: { "contents": SerField { key_py: Py( 0x00007f42caff5940, ), alias: None, alias_py: None, serializer: Some( Function( FunctionPlainSerializer { func: Py( 0x00007f42c639ca80, ), name: "plain_function[serialize]", function_name: "serialize", return_serializer: Any( AnySerializer, ), fallback_serializer: None, when_used: Always, is_field_serializer: false, info_arg: false, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "TakeSnapshot", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionTakeSnapshot", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionTakeSnapshot", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63df6f0, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63df720, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "contents", lookup_key: Simple { key: "contents", py_key: Py( 0x00007f42c6158870, ), path: LookupPath( [ S( "contents", Py( 0x00007f42c6158830, ), ), ], ), }, name_py: Py( 0x00007f42caff5940, ), validator: FunctionAfter( FunctionAfterValidator { validator: Union( UnionValidator { mode: Smart, choices: [ ( Str( StrValidator { strict: false, coerce_numbers_to_str: false, }, ), None, ), ( Bytes( BytesValidator { strict: false, bytes_mode: ValBytesMode { ser: Utf8, }, }, ), None, ), ], custom_error: None, strict: false, name: "union[str,bytes]", }, ), func: Py( 0x00007f42c639cc40, ), config: Py( 0x00007f42c61585c0, ), name: "function-after[validate(), union[str,bytes]]", field_name: None, info_arg: false, }, ), frozen: false, }, ], model_name: "TakeSnapshot", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934b0370, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "TakeSnapshot", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63df750, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63df780, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73dcf70, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "take_snapshot": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73dcf70, ), ], }, expected_repr: "'take_snapshot'", name: "literal['take_snapshot']", }, ), validate_default: false, copy_default: false, name: "default[literal['take_snapshot']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionTakeSnapshot", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9354b6b0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionTakeSnapshot", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.take_snapshot.TakeSnapshot, type: Literal['take_snapshot'] = 'take_snapshot') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: TakeSnapshot[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=TakeSnapshot, required=True), 'type': FieldInfo(annotation=Literal['take_snapshot'], required=False, default='take_snapshot')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['take_snapshot'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionViewIsometric(**data)[source][source]

The response to the ‘ViewIsometric’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.view_isometric.ViewIsometric'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['view_isometric']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>, 'config': {'title': 'OptionViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionViewIsometric:94483161616368', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.view_isometric.ViewIsometric'>, 'config': {'title': 'ViewIsometric'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.view_isometric.ViewIsometric'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.view_isometric.ViewIsometric'>>]}, 'ref': 'kittycad.models.view_isometric.ViewIsometric:94483161752160', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'ViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'view_isometric', 'schema': {'expected': ['view_isometric'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionViewIsometric', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93490ff0, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934b2260, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ViewIsometric", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73ded30, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "view_isometric", }, expected_py: None, name: "literal['view_isometric']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionViewIsometric", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionViewIsometric", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dcd50, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dcd80, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c637f430, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c637f530, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c63dcb70, ), path: LookupPath( [ S( "center", Py( 0x00007f42c63dcba0, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c63dcbd0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c63dcc00, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c63233b0, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c637ef30, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c63dcc30, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c63dcc60, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c637f2f0, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c637f230, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c63dcc90, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c63dccc0, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c63dccf0, ), path: LookupPath( [ S( "up", Py( 0x00007f42c63dcd20, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "ViewIsometric", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934b2260, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ViewIsometric", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dcdb0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dcde0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73ded30, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "view_isometric": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73ded30, ), ], }, expected_repr: "'view_isometric'", name: "literal['view_isometric']", }, ), validate_default: false, copy_default: false, name: "default[literal['view_isometric']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionViewIsometric", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93490ff0, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionViewIsometric", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.view_isometric.ViewIsometric, type: Literal['view_isometric'] = 'view_isometric') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ViewIsometric[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ViewIsometric, required=True), 'type': FieldInfo(annotation=Literal['view_isometric'], required=False, default='view_isometric')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['view_isometric'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionVolume(**data)[source][source]

The response to the ‘Volume’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.volume.Volume'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['volume']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>, 'config': {'title': 'OptionVolume'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionVolume'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionVolume:94483162635296', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.volume.Volume'>, 'config': {'title': 'Volume'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.volume.Volume'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.volume.Volume'>>]}, 'ref': 'kittycad.models.volume.Volume:94483161770576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'output_unit': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <enum 'UnitVolume'>, 'members': [UnitVolume.CM3, UnitVolume.FT3, UnitVolume.IN3, UnitVolume.M3, UnitVolume.YD3, UnitVolume.USFLOZ, UnitVolume.USGAL, UnitVolume.L, UnitVolume.ML], 'metadata': {'pydantic_js_functions': [<function GenerateSchema._enum_schema.<locals>.get_json_schema>]}, 'ref': 'kittycad.models.unit_volume.UnitVolume:94483150328352', 'sub_type': 'str', 'type': 'enum'}, 'type': 'model-field'}, 'volume': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Volume', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'volume', 'schema': {'expected': ['volume'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionVolume', 'type': 'model-fields'}, 'type': 'model'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee93589c20, ), serializer: Fields( GeneralFieldsSerializer { fields: { "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42cb03dcb0, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "volume", }, expected_py: None, name: "literal['volume']", }, ), }, ), ), required: true, }, "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934b6a50, ), serializer: Fields( GeneralFieldsSerializer { fields: { "volume": SerField { key_py: Py( 0x00007f42cb03dcb0, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "output_unit": SerField { key_py: Py( 0x00007f42c71c2f30, ), alias: None, alias_py: None, serializer: Some( Enum( EnumSerializer { class: Py( 0x000055ee929cd220, ), serializer: Some( Str( StrSerializer, ), ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "Volume", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionVolume", }, ), definitions=[])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionVolume", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c6166940, ), path: LookupPath( [ S( "data", Py( 0x00007f42c6166970, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "output_unit", lookup_key: Simple { key: "output_unit", py_key: Py( 0x00007f42c61855f0, ), path: LookupPath( [ S( "output_unit", Py( 0x00007f42c61855b0, ), ), ], ), }, name_py: Py( 0x00007f42c71c2f30, ), validator: StrEnum( EnumValidator { phantom: PhantomData<_pydantic_core::validators::enum_::StrEnumValidator>, class: Py( 0x000055ee929cd220, ), lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "m3": 3, "ft3": 1, "usfloz": 5, "usgal": 6, "ml": 8, "yd3": 4, "l": 7, "in3": 2, "cm3": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c69cda30, ), Py( 0x00007f42c69cda90, ), Py( 0x00007f42c69cdaf0, ), Py( 0x00007f42c69cdb50, ), Py( 0x00007f42c69cdbb0, ), Py( 0x00007f42c69cdc10, ), Py( 0x00007f42c69cdc70, ), Py( 0x00007f42c69cdcd0, ), Py( 0x00007f42c69cdd30, ), ], }, missing: None, expected_repr: "'cm3', 'ft3', 'in3', 'm3', 'yd3', 'usfloz', 'usgal', 'l' or 'ml'", strict: false, class_repr: "UnitVolume", name: "str-enum[UnitVolume]", }, ), frozen: false, }, Field { name: "volume", lookup_key: Simple { key: "volume", py_key: Py( 0x00007f42c6166850, ), path: LookupPath( [ S( "volume", Py( 0x00007f42c6166910, ), ), ], ), }, name_py: Py( 0x00007f42cb03dcb0, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Volume", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934b6a50, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Volume", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c61669a0, ), path: LookupPath( [ S( "type", Py( 0x00007f42c61669d0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42cb03dcb0, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "volume": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42cb03dcb0, ), ], }, expected_repr: "'volume'", name: "literal['volume']", }, ), validate_default: false, copy_default: false, name: "default[literal['volume']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionVolume", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee93589c20, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionVolume", }, ), definitions=[], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.volume.Volume, type: Literal['volume'] = 'volume') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: Volume[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=Volume, required=True), 'type': FieldInfo(annotation=Literal['volume'], required=False, default='volume')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type:

Returns:

Returns None if the schema is already “complete” and rebuilding was not required. If rebuilding _was_ required, returns True if rebuilding was successful, otherwise False.

classmethod model_validate(obj, *, strict=None, from_attributes=None, context=None)[source]

Validate a pydantic model instance.

Parameters:

obj (Any) – The object to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
from_attributes (Optional[bool]) – Whether to extract data from object attributes.
context (Optional[Any]) – Additional context to pass to the validator.

Raises:

ValidationError – If the object could not be validated.

Return type:

Self

Returns:

The validated model instance.

classmethod model_validate_json(json_data, *, strict=None, context=None)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/json/#json-parsing

Validate the given JSON data against the Pydantic model.

Parameters:

json_data (str | bytes | bytearray) – The JSON data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

Raises:

ValidationError – If json_data is not a JSON string or the object could not be validated.

classmethod model_validate_strings(obj, *, strict=None, context=None)[source]

Validate the given object with string data against the Pydantic model.

Parameters:

obj (Any) – The object containing string data to validate.
strict (Optional[bool]) – Whether to enforce types strictly.
context (Optional[Any]) – Extra variables to pass to the validator.

Return type:

Self

Returns:

The validated Pydantic model.

classmethod parse_file(path, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod parse_obj(obj)[source]

Return type:: Self

classmethod parse_raw(b, *, content_type=None, encoding='utf8', proto=None, allow_pickle=False)[source]

Return type:: Self

classmethod schema(by_alias=True, ref_template='#/$defs/{model}')[source]

Return type:: Dict[str, Any]

classmethod schema_json(*, by_alias=True, ref_template='#/$defs/{model}', **dumps_kwargs)[source]

Return type:: str

type: Literal['volume'][source]

classmethod update_forward_refs(**localns)[source]

Return type:: None

classmethod validate(value)[source]

Return type:: Self

class kittycad.models.ok_modeling_cmd_response.OptionZoomToFit(**data)[source][source]

The response to the ‘ZoomToFit’ endpoint

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__abstractmethods__ = frozenset({})[source]

__annotations__ = {'__class_vars__': 'ClassVar[set[str]]', '__private_attributes__': 'ClassVar[Dict[str, ModelPrivateAttr]]', '__pydantic_complete__': 'ClassVar[bool]', '__pydantic_core_schema__': 'ClassVar[CoreSchema]', '__pydantic_custom_init__': 'ClassVar[bool]', '__pydantic_decorators__': 'ClassVar[_decorators.DecoratorInfos]', '__pydantic_extra__': 'dict[str, Any] | None', '__pydantic_fields_set__': 'set[str]', '__pydantic_generic_metadata__': 'ClassVar[_generics.PydanticGenericMetadata]', '__pydantic_parent_namespace__': 'ClassVar[Dict[str, Any] | None]', '__pydantic_post_init__': "ClassVar[None | Literal['model_post_init']]", '__pydantic_private__': 'dict[str, Any] | None', '__pydantic_root_model__': 'ClassVar[bool]', '__pydantic_serializer__': 'ClassVar[SchemaSerializer]', '__pydantic_validator__': 'ClassVar[SchemaValidator | PluggableSchemaValidator]', '__signature__': 'ClassVar[Signature]', 'data': <class 'kittycad.models.zoom_to_fit.ZoomToFit'>, 'model_computed_fields': 'ClassVar[Dict[str, ComputedFieldInfo]]', 'model_config': 'ClassVar[ConfigDict]', 'model_fields': 'ClassVar[Dict[str, FieldInfo]]', 'type': typing.Literal['zoom_to_fit']}[source]

classmethod __class_getitem__(typevar_values)[source]

Return type:: type[BaseModel] | PydanticRecursiveRef

__class_vars__: ClassVar[set[str]] = {}[source]: The names of the class variables defined on the model.

__copy__()[source]

Returns a shallow copy of the model.

Return type:: Self

__deepcopy__(memo=None)[source]

Returns a deep copy of the model.

Return type:: Self

__delattr__(item)[source]

Implement delattr(self, name).

Return type:: Any

__dict__[source]

__eq__(other)[source]

Return self==value.

Return type:: bool

property __fields_set__: set[str][source]

classmethod __get_pydantic_core_schema__(source, handler, /)[source]

Hook into generating the model’s CoreSchema.

Parameters:

source (type[BaseModel]) – The class we are generating a schema for. This will generally be the same as the cls argument if this is a classmethod.
handler (GetCoreSchemaHandler) – A callable that calls into Pydantic’s internal CoreSchema generation logic.

Return type:

Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]

Returns:

A pydantic-core CoreSchema.

classmethod __get_pydantic_json_schema__(core_schema, handler, /)[source]

Hook into generating the model’s JSON schema.

Parameters:

core_schema (Union[AnySchema, NoneSchema, BoolSchema, IntSchema, FloatSchema, DecimalSchema, StringSchema, BytesSchema, DateSchema, TimeSchema, DatetimeSchema, TimedeltaSchema, LiteralSchema, EnumSchema, IsInstanceSchema, IsSubclassSchema, CallableSchema, ListSchema, TupleSchema, SetSchema, FrozenSetSchema, GeneratorSchema, DictSchema, AfterValidatorFunctionSchema, BeforeValidatorFunctionSchema, WrapValidatorFunctionSchema, PlainValidatorFunctionSchema, WithDefaultSchema, NullableSchema, UnionSchema, TaggedUnionSchema, ChainSchema, LaxOrStrictSchema, JsonOrPythonSchema, TypedDictSchema, ModelFieldsSchema, ModelSchema, DataclassArgsSchema, DataclassSchema, ArgumentsSchema, CallSchema, CustomErrorSchema, JsonSchema, UrlSchema, MultiHostUrlSchema, DefinitionsSchema, DefinitionReferenceSchema, UuidSchema, ComplexSchema]) – A pydantic-core CoreSchema. You can ignore this argument and call the handler with a new CoreSchema, wrap this CoreSchema ({'type': 'nullable', 'schema': current_schema}), or just call the handler with the original schema.
handler (GetJsonSchemaHandler) – Call into Pydantic’s internal JSON schema generation. This will raise a pydantic.errors.PydanticInvalidForJsonSchema if JSON schema generation fails. Since this gets called by BaseModel.model_json_schema you can override the schema_generator argument to that function to change JSON schema generation globally for a type.

Return type:

Returns:

A JSON schema, as a Python object.

__getattr__(item)[source]

Return type:: Any

__getstate__()[source]

Helper for pickle.

Return type:: dict[Any, Any]

__hash__ = None[source]

__init__(**data)[source]

Create a new model by parsing and validating input data from keyword arguments.

Raises [ValidationError][pydantic_core.ValidationError] if the input data cannot be validated to form a valid model.

self is explicitly positional-only to allow self as a field name.

__iter__()[source]

So dict(model) works.

Return type:: Generator[Tuple[str, Any], None, None]

__module__ = 'kittycad.models.ok_modeling_cmd_response'[source]

__pretty__(fmt, **kwargs)[source]

Used by devtools (https://python-devtools.helpmanual.io/) to pretty print objects.

Return type:: Generator[Any, None, None]

__private_attributes__: ClassVar[Dict[str, ModelPrivateAttr]] = {}[source]: Metadata about the private attributes of the model.

__pydantic_complete__: ClassVar[bool] = True[source]: Whether model building is completed, or if there are still undefined fields.

__pydantic_core_schema__: ClassVar[CoreSchema] = {'definitions': [{'cls': <class 'kittycad.models.point3d.Point3d'>, 'config': {'title': 'Point3d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point3d.Point3d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point3d.Point3d'>>]}, 'ref': 'kittycad.models.point3d.Point3d:94483152404640', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point3d', 'type': 'model-fields'}, 'type': 'model'}], 'schema': {'cls': <class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>, 'config': {'title': 'OptionZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit'>>]}, 'ref': 'kittycad.models.ok_modeling_cmd_response.OptionZoomToFit:94483162122368', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'data': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.zoom_to_fit.ZoomToFit'>, 'config': {'title': 'ZoomToFit'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.zoom_to_fit.ZoomToFit'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.zoom_to_fit.ZoomToFit'>>]}, 'ref': 'kittycad.models.zoom_to_fit.ZoomToFit:94483161779520', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'settings': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.camera_settings.CameraSettings'>, 'config': {'title': 'CameraSettings'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.camera_settings.CameraSettings'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.camera_settings.CameraSettings'>>]}, 'ref': 'kittycad.models.camera_settings.CameraSettings:94483154302576', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'center': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'fov_y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'orientation': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'cls': <class 'kittycad.models.point4d.Point4d'>, 'config': {'title': 'Point4d'}, 'custom_init': False, 'metadata': {'pydantic_js_annotation_functions': [], 'pydantic_js_functions': [functools.partial(<function modify_model_json_schema>, cls=<class 'kittycad.models.point4d.Point4d'>, title=None), <bound method BaseModel.__get_pydantic_json_schema__ of <class 'kittycad.models.point4d.Point4d'>>]}, 'ref': 'kittycad.models.point4d.Point4d:94483154298496', 'root_model': False, 'schema': {'computed_fields': [], 'fields': {'w': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'x': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'y': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}, 'z': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'float'}, 'type': 'model-field'}}, 'model_name': 'Point4d', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'ortho': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'type': 'bool'}, 'type': 'model-field'}, 'ortho_scale': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': None, 'schema': {'schema': {'type': 'float'}, 'type': 'nullable'}, 'type': 'default'}, 'type': 'model-field'}, 'pos': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}, 'up': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'schema_ref': 'kittycad.models.point3d.Point3d:94483152404640', 'type': 'definition-ref'}, 'type': 'model-field'}}, 'model_name': 'CameraSettings', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}}, 'model_name': 'ZoomToFit', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'model-field'}, 'type': {'metadata': {'pydantic_js_annotation_functions': [<function get_json_schema_update_func.<locals>.json_schema_update_func>], 'pydantic_js_functions': []}, 'schema': {'default': 'zoom_to_fit', 'schema': {'expected': ['zoom_to_fit'], 'type': 'literal'}, 'type': 'default'}, 'type': 'model-field'}}, 'model_name': 'OptionZoomToFit', 'type': 'model-fields'}, 'type': 'model'}, 'type': 'definitions'}[source]: The core schema of the model.

__pydantic_custom_init__: ClassVar[bool] = False[source]: Whether the model has a custom __init__ method.

__pydantic_decorators__: ClassVar[_decorators.DecoratorInfos] = DecoratorInfos(validators={}, field_validators={}, root_validators={}, field_serializers={}, model_serializers={}, model_validators={}, computed_fields={})[source]: Metadata containing the decorators defined on the model. This replaces Model.__validators__ and Model.__root_validators__ from Pydantic V1.

__pydantic_extra__: dict[str, Any] | None[source]: A dictionary containing extra values, if [extra][pydantic.config.ConfigDict.extra] is set to 'allow'.

__pydantic_fields_set__: set[str][source]: The names of fields explicitly set during instantiation.

__pydantic_generic_metadata__: ClassVar[_generics.PydanticGenericMetadata] = {'args': (), 'origin': None, 'parameters': ()}[source]: Metadata for generic models; contains data used for a similar purpose to __args__, __origin__, __parameters__ in typing-module generics. May eventually be replaced by these.

classmethod __pydantic_init_subclass__(**kwargs)[source]

This is intended to behave just like __init_subclass__, but is called by ModelMetaclass only after the class is actually fully initialized. In particular, attributes like model_fields will be present when this is called.

This is necessary because __init_subclass__ will always be called by type.__new__, and it would require a prohibitively large refactor to the ModelMetaclass to ensure that type.__new__ was called in such a manner that the class would already be sufficiently initialized.

This will receive the same kwargs that would be passed to the standard __init_subclass__, namely, any kwargs passed to the class definition that aren’t used internally by pydantic.

Parameters:: **kwargs (Any) – Any keyword arguments passed to the class definition that aren’t used internally by pydantic.
Return type:: None

__pydantic_parent_namespace__: ClassVar[Dict[str, Any] | None] = None[source]: Parent namespace of the model, used for automatic rebuilding of models.

__pydantic_post_init__: ClassVar[None | Literal['model_post_init']] = None[source]: The name of the post-init method for the model, if defined.

__pydantic_private__: dict[str, Any] | None[source]: Values of private attributes set on the model instance.

__pydantic_root_model__: ClassVar[bool] = False[source]: Whether the model is a [RootModel][pydantic.root_model.RootModel].

__pydantic_serializer__: ClassVar[SchemaSerializer] = SchemaSerializer(serializer=Model( ModelSerializer { class: Py( 0x000055ee9350c880, ), serializer: Fields( GeneralFieldsSerializer { fields: { "data": SerField { key_py: Py( 0x00007f42cb0390a0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee934b8d40, ), serializer: Fields( GeneralFieldsSerializer { fields: { "settings": SerField { key_py: Py( 0x00007f42c9e237b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d97670, ), serializer: Fields( GeneralFieldsSerializer { fields: { "fov_y": SerField { key_py: Py( 0x00007f42c724ccf0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "pos": SerField { key_py: Py( 0x00007f42cb03c250, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "orientation": SerField { key_py: Py( 0x00007f42c68895b0, ), alias: None, alias_py: None, serializer: Some( Model( ModelSerializer { class: Py( 0x000055ee92d96680, ), serializer: Fields( GeneralFieldsSerializer { fields: { "w": SerField { key_py: Py( 0x00007f42cb03f5f8, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "x": SerField { key_py: Py( 0x00007f42cb03df18, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "y": SerField { key_py: Py( 0x00007f42cb03f658, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, "z": SerField { key_py: Py( 0x00007f42cb03f688, ), alias: None, alias_py: None, serializer: Some( Float( FloatSerializer { inf_nan_mode: Null, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 4, }, ), has_extra: false, root_model: false, name: "Point4d", }, ), ), required: true, }, "ortho_scale": SerField { key_py: Py( 0x00007f42c68888f0, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42caf48100, ), ), serializer: Nullable( NullableSerializer { serializer: Float( FloatSerializer { inf_nan_mode: Null, }, ), }, ), }, ), ), required: true, }, "ortho": SerField { key_py: Py( 0x00007f42c724ce40, ), alias: None, alias_py: None, serializer: Some( Bool( BoolSerializer, ), ), required: true, }, "up": SerField { key_py: Py( 0x00007f42c9cf78a0, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, "center": SerField { key_py: Py( 0x00007f42ca508150, ), alias: None, alias_py: None, serializer: Some( Recursive( DefinitionRefSerializer { definition: "...", retry_with_lax_check: true, }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 7, }, ), has_extra: false, root_model: false, name: "CameraSettings", }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 1, }, ), has_extra: false, root_model: false, name: "ZoomToFit", }, ), ), required: true, }, "type": SerField { key_py: Py( 0x00007f42cb03da58, ), alias: None, alias_py: None, serializer: Some( WithDefault( WithDefaultSerializer { default: Default( Py( 0x00007f42c73df170, ), ), serializer: Literal( LiteralSerializer { expected_int: {}, expected_str: { "zoom_to_fit", }, expected_py: None, name: "literal['zoom_to_fit']", }, ), }, ), ), required: true, }, }, computed_fields: Some( ComputedFields( [], ), ), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None, }, required_fields: 2, }, ), has_extra: false, root_model: false, name: "OptionZoomToFit", }, ), definitions=[Model(ModelSerializer { class: Py(0x55ee92bc80a0), serializer: Fields(GeneralFieldsSerializer { fields: {"z": SerField { key_py: Py(0x7f42cb03f688), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "y": SerField { key_py: Py(0x7f42cb03f658), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }, "x": SerField { key_py: Py(0x7f42cb03df18), alias: None, alias_py: None, serializer: Some(Float(FloatSerializer { inf_nan_mode: Null })), required: true }}, computed_fields: Some(ComputedFields([])), mode: SimpleDict, extra_serializer: None, filter: SchemaFilter { include: None, exclude: None }, required_fields: 3 }), has_extra: false, root_model: false, name: "Point3d" })])[source]: The pydantic-core SchemaSerializer used to dump instances of the model.

__pydantic_validator__: ClassVar[SchemaValidator | PluggableSchemaValidator] = SchemaValidator(title="OptionZoomToFit", validator=Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "data", lookup_key: Simple { key: "data", py_key: Py( 0x00007f42c63dc630, ), path: LookupPath( [ S( "data", Py( 0x00007f42c63dc660, ), ), ], ), }, name_py: Py( 0x00007f42cb0390a0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "settings", lookup_key: Simple { key: "settings", py_key: Py( 0x00007f42c6411bb0, ), path: LookupPath( [ S( "settings", Py( 0x00007f42c64136b0, ), ), ], ), }, name_py: Py( 0x00007f42c9e237b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "center", lookup_key: Simple { key: "center", py_key: Py( 0x00007f42c63dc5d0, ), path: LookupPath( [ S( "center", Py( 0x00007f42c63dc240, ), ), ], ), }, name_py: Py( 0x00007f42ca508150, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "fov_y", lookup_key: Simple { key: "fov_y", py_key: Py( 0x00007f42c63dc2a0, ), path: LookupPath( [ S( "fov_y", Py( 0x00007f42c63dc270, ), ), ], ), }, name_py: Py( 0x00007f42c724ccf0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "orientation", lookup_key: Simple { key: "orientation", py_key: Py( 0x00007f42c6412df0, ), path: LookupPath( [ S( "orientation", Py( 0x00007f42c6413d30, ), ), ], ), }, name_py: Py( 0x00007f42c68895b0, ), validator: Model( ModelValidator { revalidate: Never, validator: ModelFields( ModelFieldsValidator { fields: [ Field { name: "w", lookup_key: Simple { key: "w", py_key: Py( 0x00007f42cb03f5f8, ), path: LookupPath( [ S( "w", Py( 0x00007f42cb03f5f8, ), ), ], ), }, name_py: Py( 0x00007f42cb03f5f8, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "x", lookup_key: Simple { key: "x", py_key: Py( 0x00007f42cb03f628, ), path: LookupPath( [ S( "x", Py( 0x00007f42cb03f628, ), ), ], ), }, name_py: Py( 0x00007f42cb03df18, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py( 0x00007f42cb03f658, ), path: LookupPath( [ S( "y", Py( 0x00007f42cb03f658, ), ), ], ), }, name_py: Py( 0x00007f42cb03f658, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py( 0x00007f42cb03f688, ), path: LookupPath( [ S( "z", Py( 0x00007f42cb03f688, ), ), ], ), }, name_py: Py( 0x00007f42cb03f688, ), validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), frozen: false, }, ], model_name: "Point4d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d96680, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "Point4d", }, ), frozen: false, }, Field { name: "ortho", lookup_key: Simple { key: "ortho", py_key: Py( 0x00007f42c63dc3c0, ), path: LookupPath( [ S( "ortho", Py( 0x00007f42c63dc390, ), ), ], ), }, name_py: Py( 0x00007f42c724ce40, ), validator: Bool( BoolValidator { strict: false, }, ), frozen: false, }, Field { name: "ortho_scale", lookup_key: Simple { key: "ortho_scale", py_key: Py( 0x00007f42c6413930, ), path: LookupPath( [ S( "ortho_scale", Py( 0x00007f42c64129f0, ), ), ], ), }, name_py: Py( 0x00007f42c68888f0, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42caf48100, ), ), on_error: Raise, validator: Nullable( NullableValidator { validator: Float( FloatValidator { strict: false, allow_inf_nan: true, }, ), name: "nullable[float]", }, ), validate_default: false, copy_default: false, name: "default[nullable[float]]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, Field { name: "pos", lookup_key: Simple { key: "pos", py_key: Py( 0x00007f42c63dc330, ), path: LookupPath( [ S( "pos", Py( 0x00007f42c63dc300, ), ), ], ), }, name_py: Py( 0x00007f42cb03c250, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, Field { name: "up", lookup_key: Simple { key: "up", py_key: Py( 0x00007f42c63dc360, ), path: LookupPath( [ S( "up", Py( 0x00007f42c63dc570, ), ), ], ), }, name_py: Py( 0x00007f42c9cf78a0, ), validator: DefinitionRef( DefinitionRefValidator { definition: "...", }, ), frozen: false, }, ], model_name: "CameraSettings", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee92d97670, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "CameraSettings", }, ), frozen: false, }, ], model_name: "ZoomToFit", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee934b8d40, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "ZoomToFit", }, ), frozen: false, }, Field { name: "type", lookup_key: Simple { key: "type", py_key: Py( 0x00007f42c63dc690, ), path: LookupPath( [ S( "type", Py( 0x00007f42c63dc6c0, ), ), ], ), }, name_py: Py( 0x00007f42cb03da58, ), validator: WithDefault( WithDefaultValidator { default: Default( Py( 0x00007f42c73df170, ), ), on_error: Raise, validator: Literal( LiteralValidator { lookup: LiteralLookup { expected_bool: None, expected_int: None, expected_str: Some( { "zoom_to_fit": 0, }, ), expected_py_dict: None, expected_py_values: None, values: [ Py( 0x00007f42c73df170, ), ], }, expected_repr: "'zoom_to_fit'", name: "literal['zoom_to_fit']", }, ), validate_default: false, copy_default: false, name: "default[literal['zoom_to_fit']]", undefined: Py( 0x00007f42c8fca310, ), }, ), frozen: false, }, ], model_name: "OptionZoomToFit", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true, }, ), class: Py( 0x000055ee9350c880, ), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py( 0x00007f42c8fca310, ), name: "OptionZoomToFit", }, ), definitions=[Model(ModelValidator { revalidate: Never, validator: ModelFields(ModelFieldsValidator { fields: [Field { name: "x", lookup_key: Simple { key: "x", py_key: Py(0x7f42cb03f628), path: LookupPath([S("x", Py(0x7f42cb03f628))]) }, name_py: Py(0x7f42cb03df18), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "y", lookup_key: Simple { key: "y", py_key: Py(0x7f42cb03f658), path: LookupPath([S("y", Py(0x7f42cb03f658))]) }, name_py: Py(0x7f42cb03f658), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }, Field { name: "z", lookup_key: Simple { key: "z", py_key: Py(0x7f42cb03f688), path: LookupPath([S("z", Py(0x7f42cb03f688))]) }, name_py: Py(0x7f42cb03f688), validator: Float(FloatValidator { strict: false, allow_inf_nan: true }), frozen: false }], model_name: "Point3d", extra_behavior: Ignore, extras_validator: None, strict: false, from_attributes: false, loc_by_alias: true }), class: Py(0x55ee92bc80a0), post_init: None, frozen: false, custom_init: false, root_model: false, undefined: Py(0x7f42c8fca310), name: "Point3d" })], cache_strings=True)[source]: The pydantic-core SchemaValidator used to validate instances of the model.

__repr__()[source]

Return repr(self).

Return type:: str

__repr_args__()[source]

Return type:: Iterable[tuple[Optional[str], Any]]

__repr_name__()[source]

Name of the instance’s class, used in __repr__.

Return type:: str

__repr_str__(join_str)[source]

Return type:: str

__rich_repr__()[source]

Used by Rich (https://rich.readthedocs.io/en/stable/pretty.html) to pretty print objects.

Return type:: Iterable[Any | tuple[Any] | tuple[str, Any] | tuple[str, Any, Any]]

__setattr__(name, value)[source]

Implement setattr(self, name, value).

Return type:: None

__setstate__(state)[source]

Return type:: None

__signature__: ClassVar[Signature] = <Signature (*, data: kittycad.models.zoom_to_fit.ZoomToFit, type: Literal['zoom_to_fit'] = 'zoom_to_fit') -> None>[source]: The synthesized __init__ [Signature][inspect.Signature] of the model.

__slots__ = ('__dict__', '__pydantic_fields_set__', '__pydantic_extra__', '__pydantic_private__')[source]

__str__()[source]

Return str(self).

Return type:: str

_abc_impl = <_abc._abc_data object>[source]

_calculate_keys(*args, **kwargs)[source]

Return type:: Any

_check_frozen(name, value)[source]

Return type:: None

_copy_and_set_values(*args, **kwargs)[source]

Return type:: Any

classmethod _get_value(*args, **kwargs)[source]

Return type:: Any

_iter(*args, **kwargs)[source]

Return type:: Any

classmethod construct(_fields_set=None, **values)[source]

Return type:: Self

copy(*, include=None, exclude=None, update=None, deep=False)[source]

Returns a copy of the model.

!!! warning “Deprecated”: This method is now deprecated; use model_copy instead.

If you need include or exclude, use:

`py data = self.model_dump(include=include, exclude=exclude, round_trip=True) data = {**data, **(update or {})} copied = self.model_validate(data) `

Parameters:

include – Optional set or mapping specifying which fields to include in the copied model.
exclude – Optional set or mapping specifying which fields to exclude in the copied model.
update – Optional dictionary of field-value pairs to override field values in the copied model.
deep – If True, the values of fields that are Pydantic models will be deep-copied.

Returns:

A copy of the model with included, excluded and updated fields as specified.

data: ZoomToFit[source]

dict(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False)[source]

Return type:: Dict[str, Any]

classmethod from_orm(obj)[source]

Return type:: Self

json(*, include=None, exclude=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, encoder=PydanticUndefined, models_as_dict=PydanticUndefined, **dumps_kwargs)[source]

Return type:: str

model_computed_fields: ClassVar[Dict[str, ComputedFieldInfo]] = {}[source]: A dictionary of computed field names and their corresponding ComputedFieldInfo objects.

model_config: ClassVar[ConfigDict] = {'protected_namespaces': ()}[source]: Configuration for the model, should be a dictionary conforming to [ConfigDict][pydantic.config.ConfigDict].

classmethod model_construct(_fields_set=None, **values)[source]

Creates a new instance of the Model class with validated data.

Creates a new model setting __dict__ and __pydantic_fields_set__ from trusted or pre-validated data. Default values are respected, but no other validation is performed.

!!! note: model_construct() generally respects the model_config.extra setting on the provided model. That is, if model_config.extra == 'allow', then all extra passed values are added to the model instance’s __dict__ and __pydantic_extra__ fields. If model_config.extra == 'ignore' (the default), then all extra passed values are ignored. Because no validation is performed with a call to model_construct(), having model_config.extra == 'forbid' does not result in an error if extra values are passed, but they will be ignored.

Parameters:

_fields_set (Optional[set[str]]) – A set of field names that were originally explicitly set during instantiation. If provided, this is directly used for the [model_fields_set][pydantic.BaseModel.model_fields_set] attribute. Otherwise, the field names from the values argument will be used.
values (Any) – Trusted or pre-validated data dictionary.

Return type:

Self

Returns:

A new instance of the Model class with validated data.

model_copy(*, update=None, deep=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#model_copy

Returns a copy of the model.

Parameters:

update (Optional[dict[str, Any]]) – Values to change/add in the new model. Note: the data is not validated before creating the new model. You should trust this data.
deep (bool) – Set to True to make a deep copy of the model.

Return type:

Self

Returns:

New model instance.

model_dump(*, mode='python', include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump

Generate a dictionary representation of the model, optionally specifying which fields to include or exclude.

Parameters:

mode (Union[Literal['json', 'python'], str]) – The mode in which to_python should run. If mode is ‘json’, the output will only contain JSON serializable types. If mode is ‘python’, the output may contain non-JSON-serializable Python objects.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to include in the output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – A set of fields to exclude from the output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to use the field’s alias in the dictionary key if defined.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A dictionary representation of the model.

model_dump_json(*, indent=None, include=None, exclude=None, context=None, by_alias=False, exclude_unset=False, exclude_defaults=False, exclude_none=False, round_trip=False, warnings=True, serialize_as_any=False)[source]

Usage docs: https://docs.pydantic.dev/2.9/concepts/serialization/#modelmodel_dump_json

Generates a JSON representation of the model using Pydantic’s to_json method.

Parameters:

indent (Optional[int]) – Indentation to use in the JSON output. If None is passed, the output will be compact.
include (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to include in the JSON output.
exclude (Union[Set[int], Set[str], Dict[int, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], Dict[str, Union[Set[int], Set[str], Dict[int, Union[IncEx, bool]], Dict[str, Union[IncEx, bool]], bool]], None]) – Field(s) to exclude from the JSON output.
context (Optional[Any]) – Additional context to pass to the serializer.
by_alias (bool) – Whether to serialize using field aliases.
exclude_unset (bool) – Whether to exclude fields that have not been explicitly set.
exclude_defaults (bool) – Whether to exclude fields that are set to their default value.
exclude_none (bool) – Whether to exclude fields that have a value of None.
round_trip (bool) – If True, dumped values should be valid as input for non-idempotent types such as Json[T].
warnings (Union[bool, Literal['none', 'warn', 'error']]) – How to handle serialization errors. False/”none” ignores them, True/”warn” logs errors, “error” raises a [PydanticSerializationError][pydantic_core.PydanticSerializationError].
serialize_as_any (bool) – Whether to serialize fields with duck-typing serialization behavior.

Return type:

Returns:

A JSON string representation of the model.

property model_extra: dict[str, Any] | None[source]

Get extra fields set during validation.

Returns:: A dictionary of extra fields, or None if config.extra is not set to "allow".

model_fields: ClassVar[Dict[str, FieldInfo]] = {'data': FieldInfo(annotation=ZoomToFit, required=True), 'type': FieldInfo(annotation=Literal['zoom_to_fit'], required=False, default='zoom_to_fit')}[source]

Metadata about the fields defined on the model, mapping of field names to [FieldInfo][pydantic.fields.FieldInfo] objects.

This replaces Model.__fields__ from Pydantic V1.

property model_fields_set: set[str][source]

Returns the set of fields that have been explicitly set on this model instance.

Returns:

A set of strings representing the fields that have been set,: i.e. that were not filled from defaults.

classmethod model_json_schema(by_alias=True, ref_template='#/$defs/{model}', schema_generator=<class 'pydantic.json_schema.GenerateJsonSchema'>, mode='validation')[source]

Generates a JSON schema for a model class.

Parameters:

by_alias (bool) – Whether to use attribute aliases or not.
ref_template (str) – The reference template.
schema_generator (type[GenerateJsonSchema]) – To override the logic used to generate the JSON schema, as a subclass of GenerateJsonSchema with your desired modifications
mode (Literal['validation', 'serialization']) – The mode in which to generate the schema.

Return type:

Returns:

The JSON schema for the given model class.

classmethod model_parametrized_name(params)[source]

Compute the class name for parametrizations of generic classes.

This method can be overridden to achieve a custom naming scheme for generic BaseModels.

Parameters:: params (tuple[type[Any], ...]) – Tuple of types of the class. Given a generic class Model with 2 type variables and a concrete model Model[str, int], the value (str, int) would be passed to params.
Return type:: str
Returns:: String representing the new class where params are passed to cls as type variables.
Raises:: TypeError – Raised when trying to generate concrete names for non-generic models.

model_post_init(_BaseModel__context)[source]

Override this method to perform additional initialization after __init__ and model_construct. This is useful if you want to do some validation that requires the entire model to be initialized.

Return type:: None

classmethod model_rebuild(*, force=False, raise_errors=True, _parent_namespace_depth=2, _types_namespace=None)[source]

Try to rebuild the pydantic-core schema for the model.

This may be necessary when one of the annotations is a ForwardRef which could not be resolved during the initial attempt to build the schema, and automatic rebuilding fails.

Parameters:

force (bool) – Whether to force the rebuilding of the model schema, defaults to False.
raise_errors (bool) – Whether to raise errors, defaults to True.
_parent_namespace_depth (int) – The depth level of the parent namespace, defaults to 2.
_types_namespace (Optional[dict[str, Any]]) – The types namespace, defaults to None.

Return type: